analysis and modelling of energy source …
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1
ANALYSIS AND MODELLING OF ENERGY SOURCE
COMBINATIONS FOR ELECTRIC VEHICLES
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy
In the faculty of Engineering and Physical Sciences
November 2010
By
Ali Milad Jarushi
School of Electrical and Electronic Engineering
2
TABLE OF CONTENTS
LIST OF TABLES 7
LIST OF FIGURES 8
ABSTRACT 11
DECLARATION 12
COPYRIGHT STATEMENT 13
ACKNOWLEDGEMENTS 14
CHAPTER 1
ENERGY SOURCES FOR FUTURE ELECTRIC VEHICLES
15
1.1 Introduction 15
1.2 Environmental Problems and Motivation Factors 16
1.3 Overview of Electric Vehicle (EV) Battery Technologies
19
1.4 The ZEBRA Battery Technology 22
1.5 Supercapacitor system 28
1.6 Comparison of Energy Source Technologies 31
1.7 Vehicle Power-train Formats 32
1.8 The scope of the research study 34
1.9 Summary 35
CHAPTER 2
ELECTRIC VEHICLE SIMULATION MODEL
2.1 Introduction 37
3
2.2 Modelling Techniques 38
2.3 EV Simulation Model 39
2.3.1 Overview. 39
2.3.2 Vehicle Parameters-Block 2 41
2.3.3 Vehicle road load and traction forces- Block
2. 42
2.3.4 Traction machine model- Block1. 44
2.3.5 ZEBRA Battery model 46
2.3.6 Battery state-of-charge (SOC) 48
2.4 The ZEBRA Battery Model 49
2.5 Vehicle Model Validation 53
2.6 Model Calibration 60
2.6.1 Acceleration Limits 60
2.6.2 Braking Torque Limits 62
2.7 Model Sensitivity 67
2.8 Summary 70
CHAPTER 3
MULTI-BATTERY OPERATION
71
3.1 Introduction 71
3.2 Faulted cell in a ZEBRA battery 72
3.3 ZEBRA Multi-battery system 73
3.3.1 Battery Management Unit (BMI) 74
3.3.2 Multi-Battery Server (MBS) 75
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3.4 Multi-battery model 76
3.4.1 Model Layout 76
3.4.2 The control scheme 76
3.5 Model Validation 79
3.5.1 Field data analysis 79
3.5.2 Model validation 81
3.6 Model analysis 85
3.6.1 Simulation of four batteries during various
faulted operations 85
3.6.2 Impact of cell failure on a two battery system - taxi performance
92
3.7 Summary 93
CHAPTER 4
COMBINATION OF BATTERY AND SUPERCAPACITOR
94
4.1 Introduction 94
4.2 Hybridisation of vehicle energy sources 95
4.3 Published supercapacitor simulation models 97
4.3.1 Combinations of passive elements 97
4.3.2 Supercapacitor models with varying
capacitance 101
4.4 The proposed supercapacitor model 103
4.4.1 Matlab/Simulink model 103
4.4.2 Parameter temperature considerations 106
4.4.3 Thermal model 107
5
4.4.4 Matlab/Simulink supercapacitor model validation
109
4.5 Vehicle on-board energy management 114
4.5.1 Principle of energy storage 114
4.5.2 Recovered energy 114
4.5.3 Power-train losses 115
4.5.4 Control strategy 117
4.5.5 Phase compensating regulator design 117
4.6 Sizing of supercapacitor power buffer 123
4.6.1 Vehicle energy and power considerations 123
4.6.2 Supercapacitor size 123
4.6 4.6.3 Full model simulation results 127
4.7 Summary 134
CHAPTER 5
DOWNSIZIED AUXILIARY POWER UNIT IN A SERIES HYBRID POWER TRAIN CONFIGRATION
135
5.1 Introduction 135
5.2 Hybridization ratio and ICE/HPM generator rating 136
5.3 Proposed power-train 138
5.4 Series hybrid electric vehicle dynamic model representation
140
5.4.1 An ICE model 140
5.4.2 Optimisation of ICE 143
5.4.3 Power-train efficiency consideration 143
6
5.5 Case studies and simulation results 144
5.5.1 Simulation results 149
5.5.2 Comparison between hybrid and conventional vehicle
152
5.6 Summary 153
CHAPTER 6
CONCLUSION AND RECOMMENDATION FOR FURTHER WORK
155
6.1 Conclusion 155
6.2 Contribution 157
6.3 Publications arising from this thesis study 157
6.4 Recommendation for further work 158
REFERENCES 159
Word count of main text: 36,884
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LIST OF TABLES
Table No. Title Page No.
Table 2.1 Edison Van data. 53 Table 2.2 Test of energy used. 62 Table 2.3 The regenerative torque is set to different limits. 62 Table 2.4 Test regenerative torque set-point vs. Energy used, Vcellmin=1.5V. 63 Table 2.5 SOC for different internal resistance using power profile. 67 Table 2.6 SOC for different battery internal resistance using a fixed
efficiency drive system. 67 Table 2.7 SOC for different internal resistance using efficiency map drive
system. 68 Table 3.1 Starting data of main parameters of the four batteries. 78 Table 3.2 The impact of different SOC of the third battery on the vehicle
performance. 86 Table 3.3 The impact of cell failure on the vehicle performance. 86 Table 3.4 Different cases of cell failure. 88 Table 3.5 Progressive cases of cell failures. 90 Table 3.6 The impact of cell failure on vehicle range. 91 Table 4.1 Thermal parameters for Maxwell 3000F cell supercapacitor. 107 Table 4.2 Example comparison of simulated and measured energies for the
charge-discharge current profile illustrated in Fig. 4.19. 111 Table 4.3 3.5 Tonne vehicle and power-train parameters for simulation
model. 123 Table 4.4 DESERVE ZEBRA battery parameters. 123 Table 4.5 Simulated range versus the voltage variation (DV). 128 Table 4.6 The range of the three currents with battery SOC. 129 Table 5.1 London taxi LTI TX4 specification of two made (Auto and
Manual) over NEDC. 144
Table 5.2 Actual and scaled 75kW Toyota Prius over NEDC. 144 Table 5.3 Summary of power requirements for proposed vehicle driving
profiles. 146 Table 5.4 Comparison of the simulation results between pure electric and
hybrid vehicle modes of Case 1. 149 Table 5.5 Comparison of the simulation results between pure electric and
hybrid vehicle modes of Case 2. 149
Table 5.6 Comparison of the simulation results between pure electric and hybrid vehicle modes of Case 3(inner city driving ECE 15). 151
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LIST OF FIGURES
Figure No. Title Page No.
Fig. 1.1 Transport and world total oil consumption. 17 Fig. 1.2 DEFRA study in United Kingdom. 17 Fig. 1.3 The Cell of ZEBRA battery. 22 Fig. 1.4 ZEBRA battery, Beta-alumina cells. 24 Fig. 1.5 Possible electrical equivalent circuit representations for ZEBRA
cells. 26 Fig. 1.6 Z5C standard battery. 26 Fig. 1.7 ZEBRA battery system. 26 Fig.1.8 Z5C battery performance. 27 Fig.1.9 Z5C Discharging. 27 Fig.1.10 Energy density and power density for different technologies 29 Fig .1.11 Maxwell supercapacitor. 29 Fig. 1.12 Comparison of battery energy and power density for different
technologies. 31 Fig. 1.13 Parallel hybrid-electric configuration. 33 Fig. 1.14 Series hybrid-electric vehicle power-train configuration. 33 Fig. 1.15 hybrid energy source, pure EV configuration. 34 Fig. 2.1 High level representation of electric vehicle simulation model. 40 Fig. 2.2 Velocity and gradient profiles for some driving cycles. 41 Fig. 2.3 Forces acting against the vehicle when starting and when in
motion 42 Fig. 2.4 The efficiency map as a 2-D Look-up table in the model. 44 Fig. 2.5 Several battery models. 46 Fig. 2.6 The layout of ZEBRA battery model. 49 Fig. 2.7 The flowchart of the model process. 49 Fig. 2.8 ZEBRA model in Simulink environment. 50 Fig. 2.9 The results of Look-up table for the internal resistance 50 Fig. 2.10 The results of Look-up table for open circuit voltage 50 Fig. 2.11 ZEBRA model over repetitive NEDC driving cycle. 51 Fig. 2.12 The measured power time profile for the SEV Edison Van used
for case (i) of the validation study. 53 Fig. 2.13 The calculated and measured SOC. 54 Fig. 2.14 The calculated and measured terminal voltage. . 54 Fig.2.15 Measured test data from the Edison Van. 56 Fig.2.16 SOC results for case (ii) the fixed efficiency drive system test
before calibration. . 57 Fig. 2.17 Terminal voltage results for case (ii) the fixed efficiency drive
system test before calibration. 57 Fig. 2.18 SOC results for case (iii) the efficiency map drive system test. 58 Fig. 2.19 Terminal voltage results for case (iii) the efficiency map test 59 Fig.2.20 Edison Van acceleration calculated from the vehicle measured
velocity data. 60 Fig. 2.21 Acceleration limiter to correct measurement. 60 Fig.2.22 The calibration of the acceleration. 61 Fig. 2.23 SOC when regerative torque limited to zero (no regen.). 62 Fig 2.24 SOC at different regenerative torque set-points. 63 Fig. 2.25 Results of regenerative torque set-points from 0 to 75 Nm. 64
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Fig. 2.26 SOC when the regerative torque set-point is 35Nm. 64 Fig. 2.27 SOC under a fixed efficiency drive system after calibration. 65 Fig. 2.28 Example of terminal voltage for fixed efficiency drives system
after calibration. 65 Fig. 2.29 SOC for different battery internal resistance. 67 Fig. 2.30 SOC for different battery internal resistance using a fixed
efficiency drive system. 68 Fig. 2.31 SOC for different internal resistance using efficiency map drive
system. 68 Fig. 3.1 A Multi-battery system. 73 Fig. 3.2 ZEBRA Z5C Management unit (BMI) system. 73 Fig. 3.3 BMI Operation in a vehicle Traction System. 74 Fig. 3.4 Operation scheme of the MBS and BMI’s. 74 Fig. 3.5 The layout of the multi-battery model. 76 Fig. 3.6 The battery parameters of four batteries in multi-battery model. 76 Fig. 3.7 MBI model using digital circuits. 77 Fig. 3.8 Flow chart of MBS operation. 77 Fig. 3.9 Currents measurements of four batteries exhibiting out of
balance. 79 Fig. 3.10 Measurement voltages across each battery. 79 Fig. 3.11 The SOC’s of the four batteries. 79 Fig. 3.12 Demand current profile. 81 Fig. 3.13 Simulated contactor states of the four batteries. 81 Fig. 3.14 Simulated and experimental voltage of battery 2. 82 Fig. 3.15 Simulated and experimental data of voltage of battery 3. 82 Fig. 3.16 Multi-battery model validation. 83 Fig. 3.17 Results of case (1). 85 Fig. 3.18 Results of case (2). 87 Fig. 3.19 The impact of cell failure on the vehicle performance (or
operation time). 88 Fig. 3.20 Results of case (3). 89 Fig. 3.21 The impact of progressive cell failure on the vehicle
performance. 90 Fig. 4.1 Traction System layout for a all-electric vehicle. 95 Fig. 4.2 Classical capacitor model. 97 Fig.4.3 A dynamic model for supercapacitor cell. 98 Fig. 4.4 Example of model layout using complex impedances. 98 Fig. 4.5 Approximation of Z(j ω ) via n RC circuits. 99 Fig. 4.6 Circuit scheme of Debye polarization cell. 100 Fig. 4.7 Equivalent circuit model for supercapacitor incorporating
variable capacitance. 101
Fig. 4.8 A simplified version of the model of Fig. 4.7 for power electronic circuit applications.
101
Fig. 4.9 Maxwell model presented in [135]. 102 Fig. 4.10 Maxwell model in Simpower Matlab. 103 Fig. 4.11 Details of the non-linear capacitance function block showing the
look up table. 104 Fig.4.12 Supercapacitor cell non-linear capacitance versus voltage
function determined from test. 105 Fig. 4.13 Reported supercapacitor parameter variation with temperature. 106 Fig. 4.14 Supercapacitor thermal equivalent circuit model. 107 Fig. 4.15 Comparison between published and simulation model results. 108 Fig. 4.16 Power circuit schematic of supercapacitor module test facility. 110
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Fig. 4.17 Picture gallery of supercapacitor module test facility. 110 Fig. 4.18 Maxwell 165F module DC voltage variation due to alternate
charge-discharge current from the W-L set. 111
Fig. 4.19 Maxwell 165F module DC voltage and current variation due to alternate charge-discharge current from the W-L set.
111
Fig. 4.20 Comparison of simulated and measured temperature of Maxwell module during repetitive cycle regime as Fig. 4.18.
112
Fig. 4.21 Electrical losses in the power-train. 114 Fig. 4.22 The converter gain for DC-to-DC converter. 115 Fig. 4.23 Regulated DC link voltage control scheme. 117 Fig. 4.24 Poles and zeros of the plant. 118 Fig. 4.25 Open-loop Bode plots of the plant. 119 Fig.4.26 Open-loop plant response. 119 Fig. 4.27 Poles and zeros of the system. 120 Fig. 4.28 Close-loop system response. 121 Fig. 4.29 Close-loop Bold plot of the system. 121 Fig. 4.30 ESERVE_ECE15 driving cycle. 124 Fig. 4.31 Connection scheme of three of Maxwell 165F supercapacitor
modules into 146V, 56 F bank. 126 Fig. 4.32 The case study of the combination. 126 Fig. 4.33 Terminal voltages of both energy sources. 127 Fig. 4.34 Currents of demand, battery, and supercapacitor. 127 Fig. 4.35 The range of the vehicle as the terminal voltage variation
tightened. 128 Fig. 4.36 The impact on the range when battery current limit is decreased. 129 Fig. 4.37 Voltages when battery current limit is 75A. 130 Fig. 4.38 Voltages when battery current limit is 50A. 131 Fig. 4.39 Voltages when battery current limit is 25A. 132 Fig. 5.1 Hybridisation of battery and ICE strategy in HEV. 135 Fig. 5.2 Method to split power between two energy sources. 135 Fig. 5.3 A series hybrid vehicle. 137 Fig. 5.4 The vehicle power-train in a series hybrid configuration format. 139 Fig. 5.5 Simulink ICE model. 140 Fig. 5.6 Example of ICE model outputs over typical NEDC driving cycle. 141 Fig. 5.7 Engine emission for different torques. 142 Fig. 5.8 Matlab/Simulink traction machine dynamic model. 143 Fig. 5.9 Reference duty profiles for the simulation study. 145 Fig. 5.10 Hybridization Ratio plot for Case 1 and 2. 147 Fig. 5.11 Power demand from each energy source component. 150 Fig. 5.12 Comparison between the all electric, hybrid (3 kW, 14.5 kW),
and pure conventional vehicle Cases over Sub-Urban NEDC. 152
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ABSTRACT
The objective of this research is to develop suitable models to simulate and analyse Electrical
Vehicle (EV) power-trains to identify and improve some of the deficiencies of EVs and
investigate new system architectures.
Although some electro-chemical batteries improvements have lately been achieved in
specific-energy, the power density is still low. Therefore, an efficient, cost-effective and
high power density support unit could facilitate EV competitiveness compared to
conventional internal combustion engine powered vehicles in the near future.
The Na-Ni-Cl2, or ZEBRA battery as it is most commonly known, has good energy and
power densities; it is very promising electro-chemical battery candidate for EV’s. The thesis
presents a detail simulation model for the ZEBRA technology and investigates its application
in an EV power-train with regard to state-of-charge and voltage transients. Unlike other
battery systems, the ZEBRA technology can sustain about 5-10% of failed cells. While this
is advantageous in single series string or single battery operation it is problematic when
higher numbers of batteries are connected in parallel. The simulation model is used to
investigate faulted operation of parallel battery configurations.
A non-linear capacitance versus voltage function is implemented for the supercapacitor
model which yields good energy and terminal voltage predictions when the supercapacitor is
cycled over dynamic regimes common to EV applications. A thermal model is also included.
Multiple energy source systems are modelled and studied in the form of an energy dense
ZEBRA battery connected in parallel with a power dense supercapacitor system. The
combination is shown to increase available power, reduce the maximum power demanded
from the battery and decrease battery internal power loss. Consequently, battery life would
be increased and more energy would be recovered from regenerative braking, enhancing the
energy conversion efficiency of the power-train.
A combination of ICE and ZEBRA battery is implemented as a range extender for London
taxi driving from Manchester to London. The hybridisation ratio of the system is discussed
and applied to fulfil the requirement with minimum emissions.
This study offers a suitable model for different energy sources, and then optimises the
vehicle energy storage combination to realize its full potential. The developed model is used
to assess different energy source combinations in order to achieve an energy efficient
combination that provides an improved vehicle performance, and, importantly, to understand
the energy source interconnection issues in terms of energy flow and circuit transients.
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DECLARATION
No portion of the work referred to in this thesis has been submitted in support of an
application for another degree or qualification of this or any other place of learning.
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COPYRIGHT STATEMENT
i. The author of this thesis (including any appendices and/or schedules to this thesis)
owns any copyright in it (the “copyright”) and he has given The University of
Manchester the right to use such Copyright for any administrative, promotional,
educational and/or teaching purposes.
ii. Copies of this thesis, either in full or in extracts, may be made only in accordance
with the regulations of the John Rylands University Library of Manchester. Details
of these regulations may be obtained from the Librarian. This page must form part
of any such copies made.
iii. The ownership of any patents, designs, trade marks and any and all other
intellectual property rights except for the Copyright (the “Intellectual Property
Rights”) and any reproductions of copyright works, for example graphs and tables
(“Reproductions”), which may be described in this thesis, may not be owned by the
author and may be owned by third parties. Such Intellectual Property Rights and
Reproductions cannot and must not be made available for use without the prior
written permission of the owner(s) of relevant Intellectual Property Rights and/or
Reproductions.
iv. Further information on the conditions under which disclosure, publication and
exploitation of this thesis, the Copyright and any Intellectual Property Rights and/or
Reproductions described in it may take place is available from the Head of School
or Electrical and Electronic Engineering (or the Vice-President) and the Dean of the
Faculty of Life Sciences, for Faculty of Life Sciences’ candidates.
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ACKNOWLEDGEMENTS
First and foremost, I would like to thank my supervisor Dr. N. Schofield for his invaluable
encouragement, support and guidance. Nigel, without your help the completion of this
thesis would have been even more difficult to say the least and I wish you the best in your
life.
I am very grateful to DESERVE Project, for helping me to get the field data to validate my
simulation model of this project and particularly I would like to acknowledge Mr. Jan for
his support and contributions
I wish to thank my family for their unstinting support and encouragement throughout the
pursuit of my doctorate.
Special thanks go to my friend Ahmed Al-Adasani for his cooperation during my study to
publish two papers and one IET Journal for review, wishing this cooperation will continue
in the future.
Finally, I would like to thank every one in the Power Conversion Group at Manchester
University who gave me help during my study. Their help and support has been much
appreciated.
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CHAPTER 1
ENERGY SOURCES FOR FUTURE ELECTRIC
VEHICLES
1.1 Introduction
Electric vehicles (EV’s) powered by electro-chemical batteries have been in existence
since the beginning of the automotive era. At the beginning of the 20th century, electrically
powered vehicles were more reliable, safe and better quality in terms of performance and
energy conversion efficiency [1, 2]. This early advantage quickly subsided with the
internal combustion engines (ICE) which addressed the range issue that was, and still is,
problematic with electric vehicles. Essentially, electro-chemical batteries can not match the
high energy density of ICE’s even with their poor energy conversion efficiency of below
20% [2, 3].
Although ICE emissions in general continue to reduce, carbon dioxide emissions have not
reduced significantly, this against a background of increasing number of vehicles. As a
result, the ICE is increasingly becoming a target of environmental issues; in particular, low
carbon related governmental policies. These environmental issues and concerns over
sustainable energy use are the key motivating factors in the development of alternative
energy sources and power-trains for road vehicles [4].
This thesis investigates the implementation of more than one energy source in an electric
vehicle power-train to alleviate the problems faced by the limited energy density of
existing state-of-art electro-chemical batteries.
The research is linked to a UK Technology Strategy Board (TSB), low carbon vehicles
Innovation Platform Project, “Develop high Energy battery and high power Supercaps for
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all Electric Range Van Evaluation (DESERVE)” [5]. “DESERVE” investigates the
combination of a modified Sodium-Nickel-chloride, or ZEBRA, battery and high power
supercapacitor system, and has provided some technical input to thesis in the form of test
data taken at the request of the author.
The electric vehicle simulation tool developed as part of this thesis study supported the
power-train design undertaken on the DESERVE project, providing a significant input to
the detailed understanding of vehicle power-train electrical dynamics and their impact and
considerations when combining multiple energy sources in various power-train
architectures. While there are a number of similar simulation platforms available, the ones
reviewed do not simulate the terminal transients in the same detail or have good correlation
of system energy and voltages.
An introduction to the environmental issues and motivating factors focussing this research
will be given in Chapter 1, followed by a background discussion of the ZEBRA battery
technology, supercapacitor systems and ICE’s. A number of vehicle power-train formats
will be presented, again as background, and clarify terminologies. Chapter 2 will introduce
the electric vehicle model that will be used throughout the remaining thesis. Chapter 3 will
discuss issues surrounding the multiple connections of traction batteries. Chapter 4 a
ZEBRA and supercapacitor power-train study, and Chapter 5 a ZEBRA battery and ICE
combination for inner city and inter city driving. Finally, Chapter 6 will conclude the
research study.
1.2 Environmental Problems and Motivation Factors
During the last decade of the 20th century climatic changes have been recognised by the
scientific and governmental communities, with many issues related to environmental
misuse by human beings. Global warming and traffic generated emission, particularly in
the cities are degrading air quality up to the point where the physical health of the local and
global population is directly threatened [4]. One of the largest sources of gas emissions so
called ‘green house' is transportation relies almost exclusively on fossil-fuels [4]. Transport
energy use continues to rise rapidly around the world, where it has been predicted to grow
by nearly 90% between 2000 and 2030; Energy use in transport will grow especially
quickly in the developing world due to the well known correlation between transportation
energy use and gross-domestic-product (GDP). Transport is also becoming the dominant
sector in terms of oil consumption, as illustrated in Fig. 1.1. It has accounted for nearly all
growth in oil use over the last two decades and this rate is expected to continue over the
next two decades [6]. The environmental impact of increased oil usage in transport will be
considerable, most notably for CO2 emissions.
17
M
illio
n T
onn
es o
f Oil
Equ
ival
ent
(MT
OE
)
Total Transport
1971
19
76
1981
19
86
1991
19
96
2001
20
01
2011
20
16
2021
6000 5000 4000 3000 2000 1000
Fig 1.1 Transport and world total oil consumption [6].
In 2000, transport accounted for about 5 Giga tonnes (GT) of CO2 emission worldwide, or
21% of total energy related emissions. By 2030, transport CO2 is predicted to rise by 87%
to 9 GT, or 24% share of the total. This is only the CO2 developed directly by vehicles,
however the transportation share is closer to 28% if the CO2 released during fuel
production, processing and delivery to vehicles is included [6].
In 2007, the United Kingdom Department of the Environment, Food and Rural Affairs
(DEFRA) released a report discussing the emissions of greenhouse gases and carbon
dioxide. Overall, emissions still exceed domestic and Kyoto target goals, as illustrated in
Fig. 1.2 [7].
UK
Kyoto report 2001-2012
Basket of green house gases
2001-2012
Domestic Carbon dioxide
2001-2012
Carbon dioxide
Mill
ion
of
ton
ne
s
900 800 700 600 500 400 300 200 100
1990
19
92
1994
19
96
1998
20
00
2002
20
04
2006
20
08
2010
20
12
Fig. 1.2 DEFRA study in United Kingdom [7].
The major concern with road transport sector is that it relies heavily on oil and alternative
vehicular solutions appear some years away [4, 6-8].
Governments and industries have been aware of the implications of transportation emission
and fuel usage and efforts are being made to change the situation and to solve these
18
challenges. It is becoming publically acceptable to force vehicle manufacturers to develop
less polluting engines and find alternative energy sources that are cleaner than the fossil
fuels used today, as a move to satisfying the Kyoto obligations [6-9]. Hence, there is a
trend in the transport industry to replace ICE’s with alternatively fuelled engines or to use
energy sources that are less polluting from well-to-wheel [10].
There are a wide variety of different technology and policy options for reducing
transportation oil use and CO2 emissions under consideration. One of these technologies is
fuel economy improvement; including improved vehicle design and new propulsion
systems for example all-electric, hybrid-electric and fuel cell vehicles. Other technologies
are alternative fuel; bio fuels and hydrogen to name prominent contenders [1-3, 11].
Electrical propulsion systems offer the best possibility for the exploitation of new energy
sources since the power-train has a high degree of flexibility allowing many energy
sources to be interfaced to a standardised electrical propulsion system [1-3], for, example,
a fixed speed ICE driven generator could in the future be replaced by a fuel cell system
should that technology mature to a sufficient level.
The ideal scenario is to develop an electric vehicle technological solution that is
compatible with existing ICE vehicles in terms of performance and range, such a vehicle
could gain a strong global presence for future transportation [1-3, 11], and will be the
subject of power-train study in Chapter5.
Of the multitude of solutions being proposed in this field, one could consider three aspects.
A comprehensive solution is the development of more electro-chemical advanced battery
technologies, i.e. increasing the energy capacity and power capability above technologies.
This is desirable for city applications, where commuting distances are short. Several new
battery technologies have been proposed, for example based on Lithium ion combinations,
to satisfy vehicle range requirements [11-14]. However, the interesting point here is to
study the impact of driving regimes on battery age, life cycle, and cell failure on the
vehicle performance. Note cell failure multi-cell system is discussed in Chapter 3.
The second aspect is to achieve higher autonomy than battery EV’s and higher efficiency
than conventional ICE-powered vehicles, thus producing vehicles having a lower
environmental impact. Vehicles that use such a combination of different energy storage
devices are commonly known as Hybrid Electric Vehicles (HEV’s). Many studies have
considered how to increase the efficiency and lower environmental impact by employing
downsized ICE and power buffer [15-20]. Note, the vehicle simulation model discussed in
19
Chapter 2 is further developed in Chapter 5 and used to analyse the impact of a downsized
ICE employed in a range extension function for 2.5 tonne taxi.
The third interesting aspect is to reach higher energy conversion efficiencies, for example,
an electrical power-train should be able to recover regenerative braking energy, which is
otherwise wasted in case of pure ICE systems. Therefore, the integration of energy sources
that have high power density and are able to recover and release energy from braking and
other system transients is an important research area. This aspect of future vehicle power-
train development is the theme of the DESERVE project. Here, a new higher energy but
lower power density cell is used in conjunction with a supercapacitor system to improve
power-train energy conversion efficiency, DC supply stability and battery life [ 21].
1.3 Overview of Electric Vehicle (EV) Battery Technologies
The electro-chemical battery is a key technology for a future EV development. Some often
quoted drawbacks of existing state- of art batteries are that they are heavy, expensive, and
have limited energy storage capability. Currently, EV’s cannot compete in terms of range
between refuelling with their ICE counterpart. The time to refuel (recharge) is also not
comparable ranging from minutes for a typical ICE powered vehicle to hours for a suitably
sized traction battery (8 hrs for a Z5C- 21kWh ZEBRA). Improved overall energy
conversion efficiency and increasing battery cycle life are also key factors for the
commercial success of EV’s [22-24].
The automotive industry has, to-date, developed BEV’s that depends on one energy source,
electro-chemical batteries. These vehicles were initially based on lead-acid technologies
where the specific power capability has been increasing but the specific energy has only
slightly improved over the past 100 years. The technology has effectively reached a
fundamental threshold dictated by the chemical composition of the cells [1-3-24-28].
With the increased interest in EV’s, there has been a great deal of research and
development improvement of batteries, yielding new types of battery technologies with
energy densities higher than lead-acid. The introduction of new batteries with high energy
density has led to unexpected difficulties being encountered, such as the recharging time,
manufacturing infrastructure and support, and ultimately cost, all of which take time to
solve. The cost depends on the capacity of production and also the materials used in the
batteries. The use of other cheaper materials may have to be restricted for environmental
reasons, unless satisfactory routines for the use of these materials are established, such as
Cadmium. Generally, considerable progress has been reached in battery development. In
the past, a battery having a specific power of 100W/kg was seen as a good technology.
20
Now it seems to be fully possible to reach over 500W/kg and figures such as 1000W/kg are
though to be possible [23-24, 29].
Regarding to lead-acid battery, although it is out-performed by many of other technologies,
it still remains a preferred option for many EV’s developments since the technology is well
understood, there is an established manufacturing and recycling base and hence cost
benefit. The battery also has relatively good charge acceptance during regenerative
braking. However, the drawbacks of this technology are low power and energy density,
and short life time when subject to vehicle type charge-discharge cycling. Hence, the
technology is virtually limited to small EV’s, for example folk-lift trucks, mobility
vehicles, and leisure vehicles.
In the short term, Nickel-Cadmium and Nickel-Metal Hydride batteries offer some
improvement in performance on lead-acid, albeit at a higher capital cost, which is off-set to
some extent by a longer battery life. It should be noted that Nickel-Cadmium is now not a
suitable candidate technology for electric vehicle application, since the material is not
classified as environmentally safe. The European end-of-life directive (2000/53/EC) has
limited the use of heavy metal (including Ni-Cds) in all vehicles ready for market after
July 2003 [12-14, 30].
The high temperature Sodium-Sulphur and Sodium-Nickel-Chloride (ZEBRA) batteries
offer significant performance improvement. The Sodium-Nickel-Chloride battery has more
advantages because it has a slightly lower operating temperature and better freezing
(200ºC) and short-circuit failure characteristic than the Sodium-Sulphur, favouring long
series chains of cells, and batteries for high (200-1200V) voltage application. These
batteries require a vacuum insulated case and good battery thermal management because
they operate with an internal temperature range from 250º to 350ºC. While these high
operating temperatures may initially suggest a major disadvantage, it must be noted that
the typical external battery case temperature is less than 30ºC when installed in a 20ºC
ambient. The ZEBRA battery has a good energy density typically 4 times that of the best
quoted lead-acid technology. Note the ZEBRA thermal management hardware is included
in the total battery mass. The technology has no self-discharge, relative low operating cost
and good cycle energy efficiency, as discussed later in Chapter 4. The ZEBRA technology
is discussed in detail in the next section.
Lithium based batteries offer both high energy and power densities, and appear the ideal
candidate for EV’s. However, they are still in the process of development and hence the
cost is relatively high. Moreover, their life cycle is dependent upon aging from the time of
manufacturing and not on the number of charge/discharge cycles, further temperature
21
management for automotive temperature specifications (i.e.-40ºC to 100ºC) and cell
balancing requirements impact on overall fundamental cell energy and power densities[12,
14].
In the future, the metal-air batteries may offer some improvement in performance and re-
fuelling capability. The electrodes and/or electrolyte of these technologies are changed at
re-fuelling stations and recycled yielding a much faster charge time than for other
technologies. However, the special requirements for replacing the metal electrodes and/or
circulating the electrolyte are still under development, and have yet to fully demonstrated
and shown to be practical for road vehicle application [31, 32].
Reviewing world-wide progress, the Department of Energy (DOE) in the USA is involved
in a program called ‘Advanced Automotive Technologies’ (ATT), the aim of which to
develop advanced energy storage. The participants in the Partnership for a New Generation
of Vehicles (PNGV) are the leaders of this program as well as the others like Chrysler,
Ford, GM and representatives of the United States Council for automotive Research
(USCAR). High power batteries are developed for hybrid-electric vehicles (to be discussed
later) and high energy density batteries for pure EV’s. DOE supports an active program of
long-range R&D to develop advanced energy storage and related systems technologies that
will be necessary for the commercial viability of competitive all and hybrid-electric
vehicles. For electric vehicles, the US Advanced Battery Consortium (USABC) has a
contribution with DOE to develop high storage batteries such as Nickel-Metal Hydride and
Lithium-ion batteries [11-14, 23, 24]. In Japan, Toyota, Nissan, Honda and two battery
companies Lithium Battery Energy Storage Technology Research Association (LIBES)
and Panasonic EV Energy Co., are working together to develop new battery technologies.
Both Honda and Toyota use Nickel-Metal Hydride batteries while Nissan uses Lithium-ion
batteries for their current hybrid-electric fleets. In the UK, Beta Research and Development
Ltd; is a manufacturer of Sodium-Nickel Chloride (ZEBRA) batteries. They have recently
been acquired by GE in the USA specifically to make large high energy, high voltage
batteries for the recovery braking energy on large Inter-State rail power-trains, utilities
support, telecoms, and general UPS. Note, GE have re-named the ZEBRA the “NaMx”
battery [33]. The manufacturing rights to the ZEBRA technology were bought by MES-
DEA, Switzerland, in 2003, who have subsequently been bought by FIMM, and Italian
automotive components supplier. Hence, this battery technology is progressing into the
higher volume market.
22
1.4 The ZEBRA Battery Technology
The ZEBRA battery was invented by Coetzer in 1978 at the CSIR in Pretoria, an Anglo-
American Corporation based in South Africa [37]. BETA Research and Development Ltd
continued the development in the UK and was integrated into the joint venture of Daimler
and the Anglo-American Corporation. The jointly founded company, AEG Anglo batteries
GmbH, started the pilot line production of ZEBRA batteries in 1994. Later, the ZEBRA
technology was acquired in total by MED-DEA, Stabio, Switzerland industrialised
production. The production capacity in 2004 was 2000 battery packs per year in a building
designed for a capacity of 30,000 battery packs per year. It is claimed that this has resulted
in cost reductions that make the life-cycle-cost of the ZEBRA battery less than those of
lead-acid batteries equitable energy density [21-23].
In the ZEBRA technology, the electrode material is a nickel powder and plain salt, the
electrolyte and separator is β’’-Al 2O3 -ceramic that conductive for Na+ ions but insulator
for electrons [34-38]. The ZEBRA battery operates at temperature range of +270ºC to
350ºC because the Sodium-ions conductivity has a reasonable value of ≥ 0.2Ω-1 cm-1 at
260ºC and is temperature-dependent with a positive gradient [34]. The basic cell structure
is shown in Fig. 1.9 [34-36].
Fig. 1.3 The Cell of ZEBRA battery [34].
The basic cell reaction is:
NaNiClNiNaCl ingdischingCh 22 2arg/arg + →←+
(1.1)
Due to the ceramic electrolyte in ZEBRA cell there is no side reaction, hence the charge
and discharge cycle is 100% charge (or 100% columbic) efficient, which means no charge
charging
discharging
23
is lost, unlike all other electro-chemical technologies. The cathode has a porous structure
of nickel (Ni) and salt (NaCl) in a 50:50 mixture. This salt liquefies at 154ºC and, in the
liquid state; it is conductive for Sodium-ions. Features that are essential to the ZEBRA
technology are:
• Sodium-ion conductivity inside the cathode: the ZEBRA cells are produced in a
discharge state. The liquid salt (NaAlCl4) is vacuum-impregnated into the porous
Nickel-salt mixture which forms the cathode. The Sodium-ions are conducted between
the ceramic surface and the reaction zone inside the cathode bulk during charge and
discharge and makes all cathode material available for energy storage.
• Low resistive cell failure mode: The Ceramic is a brittle material and may have small
cracks during production. In case of large cracks, the liquid salt (NaAlCl4) becomes in
contact with the liquid Sodium (the melting point of which is 90ºC), the reaction is
described by:
AlNaClNaAlCl +→ 44 (1.2)
This reaction shorts the conductive current path between positive and negative so that
the cell goes to a low resistance. In case of small cracks, the salt and aluminium closes
the crack. Thus, the ZEBRA battery has some cell failure tolerance. It has been
established by Beta R&D that 5-10% of cells a series string may fail before the battery
can no longer be used. The battery controller detects cell failure, and adjusts all
operative parameters. The impact of cell failure in system containing a number of
parallel connected batteries ( to realise vehicle energy requirements) is discussed in
Chapter 3 and illustrated with consideration to 4- and 2- parallel battery system for 7.5
tonne and 2.5 tonne vehicle respectively.
• Over-charge reaction: the charge capacity of the ZEBRA cell is determined by the
quantity of salt (NaCl) available in the cathode. If the charge voltage continues to be
applied to the cell even when the cell is fully charged, the liquid salt (NaAlCl4)
supplies a sodium reserve following the reversible reaction:
234 222 NiClAlClNaNiNaAlCl ++↔+ (1.3)
This over-charge reaction requires a higher voltage than the normal charge, having
three advantages: (i) the current is stopped automatically because the open circuit
voltage increased and equalised the charger voltage, (ii) in event of cell failure, the
remaining cells can be over-charged in order to balance the voltage of the failed cells,
(iii) in case of down hill and the battery is fully charged, the battery has an over-charge
24
capacity of up to 5% for regenerative breaking so the breaking behaviour of the vehicle
is unchanged [34].
• Over-discharged reaction: from the first charge, the cell has an extra of Sodium in the
anode compartment so that for an over-discharge tolerance, Sodium is available to
maintain current flow at a lower voltage; the reaction is same as for cell failure but runs
without ceramic failure.
In the ZEBRA cell design, the positive pole is connected to the current collector, which is a
hairpin shaped wire with an inside copper core for low resistivity and an outside Nickel
plating such that all material in contact with the cathode is consistent with cell chemistry.
The cathode material is a mixture of salt with nickel powder and trace of iron and
aluminium. This mixture is filled into the Beta-alumina tube, an example of which is
illustrated in Fig. 1.10 [34-36].
Beta alumina ceramic tube with compression bond seal.
(a) Circular or ‘slim line’ cross-section. (b) Cloverleaf or ‘monolith’ cross-section.
Fig. 1.4 ZEBRA battery, Beta-alumina cells[ 34,35].
For resistance reduction, the Beta-alumina tube can be formed to increase the surface area,
the so-called monolith cell, as illustrated in Fig. 1.4 (b). The tubes are surrounded and
supported to the cell case by a 0.1 mm thick steel sheet to form a capillary gap surrounding
the tube. In the early ZEBRA cell designs, the ceramic tube was a circular cross-section, as
illustrated in Fig.1.4 (a). For the DESERVE project, Beta R&D have reverted back to this
early cell design since it exhibits a higher energy density of around 20% on the
“cloverleaf” design but lower power density. For the DESERVE vehicle power-train, this
reduction in power-density is satisfied by the inclusion of a supercapacitor system, as will
discussed in Chapter 4. Since most vehicles manufacture require a balance of energy and
power densities, all current commercial ZEBRA cell designs utilise a “cloverleaf” cross-
section tube. A recent improvement has been introduced to the cell chemistry. The
25
positive electrode in the monolith cell has been doped with iron, which is mentioned
above, exhibits a similar reaction to that of nickel [34-37]:
FeClNaNaClFe +↔+ 222 (1.4)
The new chemical composition gives a cell performance characteristic similar to that of
two independent cells, i.e. nickel and iron, connected in parallel, as illustrated in Fig. 1.5,
showing possible electrical equivalent circuit representations for the older ZEBRA cell
without iron doping (a) where ohmic effect described by Rd and Cd, waste energy is
modelled by Rw, and the new ZEBRA cell design with iron doping (b) [34, 37], the EOC’s
are the open circuit voltage for nickel and iron cells. The actual chemical reaction is an
aside as far this thesis research is concerned, but has an input in terms of the battery open-
circuit voltage characteristic with battery state-of-charge. This will be discussed in further
detail in Chapter 2, but to summaries, the open-circuit voltage of the iron-chloride (FeCl2)
component is 2.35V, while the nickel-chloride (NiCl2) component has a higher open-
circuit voltage of 2.58V. The combined cells are able to contribute a higher maximum
power [34, 38]. However, the battery internal resistance is now a more complex function
of current rate and SOC as will be discussed in Chapter 2.
The sodium is wicked to the top of the tube due to capillary force and wets it independent
of the sodium level in the anode compartment.
In Battery design, ZEBRA cells can be connected in parallel and in series. Different battery
types have been made with one to five parallel strings, up to 200 cells in series and 100-
500 cells in one battery pack. The battery Z5C, shown in Fig.1.6, has 216 cells connected
in one string which can provide open circuit voltage (EOC) of 557 V, two strings 108 cells
connected in parallel which can provide an EOC of 278V [34, 37].
26
Rd
Cd
Rw
EOCNi Vterminal_Ni
(a) ZEBRA SL09 cell without iron doping.
Rd
Cd
Rw
EOCNi
Vterminal_Ni-Fe EOCFe
RFe
(b) ZEBRA ML1C and ML1D cell - with iron doping
Fig. 1.5 Possible electrical equivalent circuit representations for ZEBRA cells.
Ventilations Outer casing (Vacuum Insulated) Battery management
unit (BMI)
Circuit breaker
Fig. 1.6 Z5C standard battery [1.39].
In the ZEBRA Battery System Design, the battery has a management interface (BMI) that
can control the ohmic heater, the fan, the main circuit breaker which connected to positive
and negative poles of the battery, as shown in Fig. 1.6. The BMI measures and supervises
voltage, current, state-of-charge (SOC), insulation resistance of positive and negative to
ground and controls the charger, as shown schematically in Fig. 1.7 [34, 37].
27
Fig. 1.7 ZEBRA battery system [34, 37].
A Multi Battery Server (MBS) is designed for up to 16 Z5C batteries connected in parallel,
yielding systems up to 285kWh/510kW [34, 37].
The ZEBRA battery has passed many stringent automotive safety testes. Tests, including
crash of an operative battery against a pole at 50km/h, over-charge, over-discharge, short
circuit, vibration, external fire and submersion of the battery in water, have been specified
and performed [34, 40, 41]. The ZEBRA battery has passed all those tests because it has
four barriers to safety, these are stated as: a barrier by chemistry, barrier by the cell case,
barrier by the thermal enclosure, and a barrier by the battery controller [40].
In terms of charging, the ZEBRA battery is charged with a current-voltage characteristic of
two regimes: the first is a normal charge at a 6 hour rate with an upper voltage limit of
2.67V per cell. The second regime is a fast charge at a 1hour rate with upper voltage limit
of 2.85V per cell; it is permitted up to 80% state-of-charge (SOC). During braking, the
regenerative is limited to 3.1V per cell and 60A per cell, as illustrated Fig. 1.8. The peak
power during discharge, defined as the power at 2/3rd’s OCV is independent of SOC and
gives a constant vehicle performance over the SOC range, as shown in Fig 1.9 [34, 37].
Fig. 1.8 Z5C battery performance [34].
28
Fig. 1.9 Z5C Discharging [34].
1.5 Supercapacitor system
Supercapacitors are now being used in many applications where higher power densities
will be required. One of these applications is for load levelling in hybrid-electric vehicles
(HEV). Another high power application is in telecommunications, where short, high-power
pulses are required, and power smoothing in elevators [42- 46].
The supercapacitor (also known as an ultracapacitor), is one such technology, which is
often referred to as a double-layer capacitor, since charge is stored in two polarised liquid
layers formed when a potential exists between two electrodes immersed in an electrolyte. It
is an electro-chemical device. However, there are no chemical reactions involved in its
energy storage mechanism.
It is reported that such electro-chemical double-layer capacitors have been developed in
Japan using solid porous carbon on either side of a porous membrane containing a dilute
sulphuric acid electrolyte which is dispersed and in intimate contact with the high surface
area electrode material [43, 44, 47]. Supercapacitors can be viewed as two non-reactive
porous plates suspended within an electrolyte, with a voltage applied across the plates. The
applied potential on the positive plate attracts the negative ions in the electrolyte, while the
potential on the negative plate attracts the positive ions. This effectively creates two layers
of capacitive storage, one where the charges are separated at the positive plate, and another
at the negative plate. Capacitors store energy in the form of separated electrical charge.
Supercapacitors provide greater area for storing charge have closer separation than other
capacitor technologies, thus they have relatively high capacitance.
A conventional capacitor gets its area from plates of a flat, conductive material. To achieve
high capacitance, this material could be in great lengths, or sometimes have a texture
imprinted to increase surface area. A conventional capacitor separates its charged plates
with a dielectric material, sometimes a plastic or paper film, or a ceramic. These dielectrics
can only be made as thin as the available films or applied materials.
A supercapacitor gets its surface area from a porous carbon-based electrode material. The
porous structure of this material allows its surface area to approach 2000 m2/g, much
greater than that which can be accomplished using flat or textured films and plates.
29
Supercapacitor charge separation distance is determined by the size of the ions in the
electrolyte which are attracted to the charged electrode. This charge separation (less than
10 angstroms) is much smaller than ones that can be accomplished using conventional
dielectric materials [50, 51].
The combination of extremely high surface area and small charge separation gives the
supercapacitor its outstanding capacitance relative to conventional capacitors.
The specific energy density of a capacitor or battery is the energy in Joules divided by the
capacitor or battery weight, and usually expressed as Watt-hour per kg (Wh/kg). The
critical characteristics of a supercapacitor are its energy density and power density (W/kg).
The energy density is dependent on the capacitance and maximum voltage, while the
power density is dependent on the equivalent series resistance (ESR) and maximum
voltage. Although supercapacitors have a low specific energy of 5.72Wh/kg, the rated peak
power capability is 17.5kW/kg [47-49]. A comparison between different technologies
regarding to energy density and power density is illustrated in Ragone plot of Fig. 1.10.
10 100 1000 10000 Power density (W/kg)
Ene
rgy
dens
ity
(Wh/
kg)
1000 100 10 1.0 0.1 0.01
Fig. 1.10 Energy density and power density for different technologies [52].
A supercapacitor cell basically consists of two electrodes, a separator, and an electrolyte. A
schematic diagram of the structure of a supercapacitor is presented in Fig. 1.11 (a).
Although it may be considered as a relatively simple layered system, it is only upon closer
consideration of the nature of each component that the real complexity is revealed.
The design of the electrodes is very important for Electro-chemical Double-Layer
Capacitance (EDLC) performance. The electrode does not only determine the capacitance
but also contributes to the equivalent series resistance (ESR). The electric charge stored in
the layer is proportional to the surface area of the electrode and reverses proportional to
thickness of the double layer. Optimizing the pore-size distribution of the electrodes
improves the high-rate charge/discharge characteristics [50, 51].
30
Electrodes
Electrolyte Separator
60.4mm
138mm
Weight=0.51 kg
(a) Supercapacitor structure. (b) 3000F Maxwell cell.
191 mm
157mm
418mm
Weight=13.5 kg
(c) 165F 48V Maxwell module.
Fig. 1.11 Maxwell supercapacitor [49].
There are two main supercapacitor categories defined with respect to their electrode
materials
• Carbon based materials: many studies have been undertaken on carbon material as the
electrode [51]. Carbon is used as an electrode material very frequently due to low cost,
high surface area and its availability. Carbon is available as powders or fibres. Both
stability and conductivity of the activated high carbon area is decreased with increasing
surface area. Moreover, activated carbon with larger pores is more suitable for high
power applications. The accessible time to pores of various sizes was correlated with
the pore size distribution of the materials [53].
• Metal oxides: metal oxides are suitable for aqueous electrolytes; hence the nominal cell
voltage is limited to 1 V [53].
A simple capacitor is supposed to be capable of discharging and charging at high rates,
limited only by a small equivalent series resistance. However, based on high specific
area porous electrode materials, power limitations arise due to the complex distribution
of electrolyte internal resistance [54]. The main electrolyte technologies used are:
• Organic: the advantage of this kind of electrolyte is the higher working voltages
that can be achieved. However, organic electrolyte has a higher specific resistance
that reduces the maximum power. Fortunately, this reduction in power is
compensated for by the higher cell voltage.
31
• Aqueous: the cost of aqueous electrolyte is much lower than organic electrolytes.
The unit cell voltage of this technology is limited to 1V; the main advantage of
aqueous electrolyte is higher conductance [53].
Separators are a key feature of the technology where is high porosity, high strength,
and ultra-thin manufacture are critical because the impedance of the electrolyte
separator is proportional to its thickness and inversely proportional to its porosity.
There are a number of polymeric separators now available. Although it is an expensive
element, it provides a low impedance and high strength with a thickness of 20~40µm
[47, 52].
Fig. 1.11 illustrates a typical commercial 300F, 2.7V supercapacitor cell from Maxwell
technologies (b) and a 48V, 165F pack that essentially equates to 18 of the 3000F cells in
series (c) [49]. Three of the 48V, 165F supercapacitor packs connected in simple series are
used in the DESERVE vehicle power-train, the sizing and characterisation of which is
discussed in Chapter 4.
1.6 Comparison of Energy Source Technologies
The choice of battery and supercapacitor technologies vehicles is difficult, and the
assessment criteria somewhat arbitrary. Data that has generally formed option and
impacted on the choice of battery for electric technology has been specific energy versus
specific power, so called Peukert data. For a pure electric vehicle, high battery energy is
generally required to fulfil the requirement for range between the recharging cycles.
With a hybrid-electric vehicle, and especially a parallel hybrid where the internal
combustion engine (ICE) has a relative low power, it is important that the battery can assist
the engine via the electrical traction machine when higher power is required. In this case, a
battery specified for high power is needed.
The safety issues should be considered in battery choice, as mentioned earlier, ZEBRA
battery has passed many stringent automotive safety testes including crash of an operative
battery against a pole at 50km/h, over-charge, over-discharge, short circuit, vibration,
external fire and submersion of the battery in water [34, 40, 41].
Specific energy performance data for various battery and supercapacitors are illustrated in
Fig. 1.12, showing energy and power densities (a) and (b) respectively and specific energy
and power (c) and (d) respectively, for comparison.
32
0
50
100
150
200
0 50 100 150 200 250 300 350
Wh/l
Wh/
kg
Hawker Pb-acid
Maxwell SC
ZEBRA NaNiCl
SAFT NiMH
SAFT NiCd
SAFT Li-ion
1
10
100
1000
10000
100000
1 10 100 1000 10000 100000
W/l
W/k
g
Hawker Pb-acid
Maxwell SC
ZEBRA NaNiCl
SAFT NiMH
SAFT NiCd
SAFT Li-ion
(a) Energy density (b) Power density
1
10
100
1000
1 10 100 1000 10000 100000
W/l
Wh/
l
Hawker Pb-acid Maxwell SC
ZEBRA NaNiCl SAFT NiMH
SAFT NiCd SAFT Li-ion
1
10
100
1000
1 10 100 1000 10000 100000
W/kg
Wh
/kg
Hawker Pb-acid
Maxwell SC
ZEBRA NaNiCl
SAFT NiMH
SAFT NiCd
SAFT Li-ion
(c) Specific energy. (d) Specific power.
Fig. 1.12 Comparison of battery energy and power density for different technologies.
1.7 Vehicle Power-train Formats.
To overcome the problems of relatively low battery energy density (compared to ICE’s)
and limited peak power (of electro-chemical batteries) to the inception of alternative
vehicle power-train technologies, hybrid-electric vehicle (HEV) and fuel cell powered
vehicles (FCV) are being developed [2, 3, 23, 55-57]. ICE/battery hybrid EV’s are
commercially available, notably the Toyota Prius and Honda Insight, whole fuel cell
vehicles appear some way from commercialisation.
Hybrid-electric vehicles generally refer to ICE and battery combination vehicles where the
main design goals can be summarised as to:
• maximise fuel economy,
• minimise emissions,
• minimise system costs,
• retain good driving performance,
• satisfy all power demands.
The peak demand power could be satisfied by choosing either the ICE or battery as a main
energy source and the other as an auxiliary energy source. The vehicle energy source is a
33
combination of energy and power-dense sources, where the energy dense source provides
energy to satisfy the requirements and the power-dense source provides the peak power for
acceleration and to sink regenerative braking energy thus alleviating the peak power
requirements from the energy-dense source [57-60].
Because an ICE’s natural output is mechanical power and any unnecessary energy
transformation is undesirable, ICE’s are usually applied in a parallel configuration where
two different energy sources, both with independent mechanical outputs that are combined
in parallel via a combinational gearbox to deliver energy to or accept energy from the
wheels, as shown in Fig. 1.13. It usually combines an internal combustion engine and an
electrical machine that is powered electrical via a source such as batteries or supercapacitor
[57, 61, 62]. For other candidate for main energy sources, such as Fuel cell, or secondary
batteries, series configuration is more suitable [23, 58].
The series hybrid-electric vehicle configuration employs a “down-sized” ICE operating at
or around a fixed speed point. The ICE output is mechanically disconnected from the
vehicle wheels and drives an electrical generator that supplies average energy to the
power-train DC-link. A battery usually provides the second energy source satisfying the
peak power demands and having some or no net energy contribution. Fig. 1.14 illustrates a
simple series hybrid-electric power-train configuration [23, 63].
The advantage of this combination is that the ICE has low fuel consumption and hence
emission and high conversion efficiency because it runs at its optimal speed and torque.
The disadvantage is lost energy due to the two stages of power conversion during the
transformation of the energy between the ICE and the wheels (ICE/generator and
generator/motor) [2, 3, 23]. However, this configuration lends itself to more advanced
power-train configurations where the ICE and associated generator may be replaced by the
fuel cell system, or alternatively fuelled (i.e. bio fuelled, hydrogen) energy converter [55,
57, 65].
Many other power-train configurations have been proposed to-date, but these are outside
the scope of this study. For this thesis, the series hybrid-electric vehicle configuration will
be studied in Chapter 5 where the down-sized ICE is employed in a range extending
function for a pure battery electric vehicle. Chapter 2 to 4 will consider pure electric
vehicles utilising more than one electrical based on-board energy sources, specifically high
energy ZEBRA batteries and supercapacitor power-train combinations, as illustrated in
Fig. 1.15.
34
Battery
ICE
Fuel tank
Torque coupler
Transmission
Inverter TM.
Traction Machine
Differential
Fig.1.13 Parallel hybrid-electric configuration.
Inverter
Battery
Rectifier
T.M.
ICE G
Differential
Fig. 1.14 Series hybrid-electric vehicle power-train configuration.
Inverter
Supercapacitor bank
Battery
T.M. Differential
Traction Machine
Fig. 1.15 hybrid energy source, pure EV configuration.
1.8 The scope of the research study.
In this study, the works have been concentrated mainly on the energy sources that could be
combined with EV energy source which is a high-temperature sodium nickel chloride
(NaNiCl) ZEBRA battery. The study is mainly for energy sources modelling. They include
investigating the appropriate characterisation test methodology and the suitable modelling
35
approach for ZEBRA battery alone or combined with another energy source, and then
optimise the energy storage combination to realize the full potential.
The vehicle model will be used to assess different energy source combinations to achieve
an energy efficient combination, to get good vehicle performance, but importantly, to
understand the energy source interconnection issues in terms of energy flow and circuit
transients.
This dynamic simulation of the power-train will be one of the novel features of this
research study, differentiating the work from other vehicle simulators, for example
ADVISOR, that tend to use more energy value models since global as apposed to dynamic
performance (i.e. speed, emissions energy) are the main calculable.
In the modelling works, the motivated aspects have been applied for several energy/power
sources technologies, i.e. ZEBRA battery, supercapacitor and ICE with PM generator are
concentrated in this study.
In introduction to the environmental issues and motivating factors focussing this research
is given in Chapter 1, followed by a background discussion of the ZEBRA battery
technology, supercapacitor systems and ICE’s. A number of vehicle power-train formats
are presented, again as background, and clarify terminologies. Chapter 2 will introduce the
electric vehicle model that will be used throughout the remaining thesis. Chapter 3 will
discuss issues surrounding the multiple connections of traction batteries. Chapter 4 a
ZEBRA and supercapacitor power-train study, and Chapter 5 a ZEBRA battery and ICE
combination for inner city and inter city driving. Finally, Chapter 6 will conclude the
research study.
1.9 Summary. Although the range of ICE vehicle is rapidly improved comparing with Electric vehicles
(EV’s), the environmental issue was, and still is, problematic with ICE emissions in
general continue to reduce, carbon dioxide emissions have not reduced significantly, this
against a background of an increasing number of vehicles.
The electro-chemical battery is a key technology for future EV development. ZEBRA
battery is one of the promising technologies in the UK, and recently it has been enquired
by GE in the USA under name of NaMx battery.
More than one energy source in an electric vehicle power-train, is an interesting
proposition especially if the combination aims to target the specific qualities of the energy
source, for example the energy density of electro-chemical battery and power density of
36
supercapacitors or electro-mechanical flywheels. Here in Chapter one, an overview has
been given of some technologies such as ZEBRA and supercapacitor, the goal is to
investigate the problems faced by the limited energy density of existing state-of-art electro-
chemical batteries. In following Chapters, combinations of limited energy battery and a
high power density source are suggested; Chapter 2 will introduce the electric vehicle
model that will be used throughout the remaining thesis, while Chapter 3 will discuss
issues surrounding the multiple parallel connections of traction batteries. In Chapter 4, a
ZEBRA and supercapacitor power-train will be studied and a ZEBRA battery and ICE
combination for inner city and inter city driving discussed in Chapter 5.
37
CHAPTER 2
ELECTRIC VEHICLE SIMULATION MODEL
2.1 Introduction.
The systems that are potentially high cost and require high research and development effort
could be simulated to reduce the cost. This is the case for EV components, since prior
background is very new and hence the production of EV components is expensive and
operational development time is long. To reduce cost, development time and processes,
and minimize the number of prototypes, the simulation of electric vehicle power-trains and
their associated energy source components is an essential step.
In this Chapter, a Matlab/Simulink model of EV power-train components is implemented
aiming to achieve the highest efficiency possible for different energy source combinations
in different power-trains. This requires models to calculate power consumption, power
losses, and energy use. The model included energy sources such a batteries and
supercapacitor, internal combustion engines (ICE). In addition, electrical traction machines
and converters form load elements in the power-train. Additional electric auxiliary sources
could be added to the model, for example a fuel cell range extender.
Today, the simulations of electric energy source components as well as other components
in the power-train are mostly dependent on characteristic functions of individual
components. However, the real behaviour is dependent on various parameters such as input
/output power, current, and voltage for each componenet, the component characteristic and
their interaction in a larger complex system. The developed model is studied and calibrated
38
against real vehicle test data from large urban electric vehicles supplied by the DESERVE
consortium.
2.2 Modelling Techniques.
Modelling techniques can be classified into two categories:
• Steady-state and dynamic modelling This kind of model describes the behaviour of the system over a certain period of time.
Steady-state models show the behaviour of each component in the power-train from a high
level to be analysed over specify drive cycles. The technique is useful for developing the
power-train structures or power-train operation such as discussed in [65] where the vehicle
model is used to optimise power-train performance and to improve efficiency.
The dynamic model describes the transient behaviour of the system and analyses the
interactions between all components in the power-train including losses that might occur
during transient loads such as braking.
• Backward-facing and forward-facing modelling
The backward-facing model considers the desired vehicle speed as the input to the model
then determines operation according to that how the system reacts. Using the kinematical
relationships of the vehicle drive-train, i.e. the vehicle mass, tire, and road characteristics,
road incline and aerodynamics is calculated the mechanical torque to accelerate the vehicle
at the input demand speed. From this point and backward to the energy source components,
performance can be calculated [66, 67, 68].
On the other hand, Forward-facing modelling takes as inputs the driver commands and,
simulating the physical behaviours of each component, generates the vehicle performance
as outputs. This forward aspect is the best considered as using the present speed as an
initial input parameter to track the desired speed using a control [67, 68].
Generally, an effective and useful vehicle simulation tool should meet the following
requirements:
• accurate representation of vehicle dynamics and estimation of the performance for a
given driving cycle,
• including many different vehicle components, vehicle configurations, and
39
• an open architecture to allow modification of any subsystem, including the ability to
create new component models from specific data.
There are many software packages available to simulate electric vehicles. Most software is
based on or able to communicate with Matlab /Simulink environments, such as ADVISOR
[2.4] and QSS-TB [69].
ADVISOR was initially developed by the National Renewable Energy Laboratory, USA,
in 1994, with many inputs from public, commercial and private sources. After some
development, the open access structure became restricted and commercialised. The
software claimed to facilitate understanding in the design electric vehicles and associated
components [68]. ADVISOR is developed as an analysis tool using fundamentally a
backwards-facing modelling technique. However, ADVISOR is lacking in some functions
needed for design; for example, it cannot be used for fast dynamic simulation due to the
fidelity of the individual model elements [68, 70].
PSIM is another software package used to simulate vehicle power system; it has been
developed to simulate power electronic switching and digital control in the vehicle systems
[71]. However, such detail becomes time cumbersome for large system simulation where
the time being simulated may run into many hours, for example, a full traction battery
discharging.
Modelica is another package used for vehicular system simulation; it is an object-oriented
modelling language. Modelica discussed in detail in [72], it is based on the physical
properties and chemical phenomena of components and, again, is unsuited to full vehicle
performance simulation and analysis.
2.3 EV Simulation Model.
2.3.1 Overview.
While there are a number of commercial simulation tools, the simulation model elements
must each have suitable resolution to model detailed dynamic operation, an important
consideration when assessing the specification requirements for the interconnection of
multiple electrical components and their associated interface power electronics.
Matlab/Simulink is a powerful and flexible programming tool specifically targeted to
technical computing tasks. It is based on Block diagram models and constitutes an
effective environment to construct complex, highly inter-related vehicle models.
The model developed as part of this thesis research, is based on the Matlab/Simulink
programming environment and simulates cases which could be presented for a number of
40
vehicle power-train components. The components can be interconnected to investigate
alternative energy sources and power-train components proposed for electric vehicles, the
combination of which is undertaken to exploit their various attributes.
The simulation model is used to integrate separate components models into a complete
vehicle model that will approximate the behaviour of the system of interest. Resulting
simulations illustrate how component choices affect the overall performance of the system
beyond the particular aspect that motivated any particular selection and hence the
simulation tool greatly enhances the learning experience. The system model presents a set
of combinations which by appropriate selection, and sizing and configuring allows some
optimisation of the vehicle components to satisfy the performance requirements.
Further in the thesis the simulation model is used to study combinations of energy and
power-dense sources. The energy-dense sources are specified and operated to fulfil the
requirements for vehicle range, while the power-dense sources provides the peak power for
acceleration or to recover regenerative braking and to alleviate the peak power
requirements of the energy-dense source ( usually either an electro-chemical battery or
ICE).
There are many options of energy source combinations for electric vehicles, in this study
the ZEBRA battery is chosen to be modelled as the main energy-dense source, since this
technology is the chosen one of the DESERVE project and test data is readily available
against which to validate the model. Supercapacitors are simulated as a part of the model to
present a power-dense option. An ICE based on available Toyota Prius data is
implemented along with a brushless permanent generator set, data for which is available
via the thesis project supervisor.
A high level representation of the electric vehicle simulation model is illustrated in Fig 2.1
consisting of inputs, energy system models and output Blocks. The input parameters are
classified into three parts:
(i) the first one is drive-train, shown in Block 1. Here, a selection can be made from
the library of traction machine with auxiliary loads. In addition, management
ON/OFF switch is used for change of energy source combination.
(ii) Block 2 is a set of inputs including standard and developed vehicle velocity profiles,
vehicle parameters that are used for mechanical torque calculation. The various
parameter sets are selected from libraries for battery and supercapacitor
respectively.
(iii) Block 3 is the control scheme to control the energy flow between energy sources
components.
41
(iv) Block 4 and 5 defines energy sources parameters, for the battery and supercapacitor
respectively such as number of cells, batteries, initial state-of-charge (SOC) etc. The
combination of energy source and their parameters are changeable depending on the
vehicle application.
(v) Finally, the output Blocks 6 and 7display detailed results such as currents, voltages,
dynamic state-of-charge, energy-in, energy-out, losses, and range.
Fig. 2.1 High level representation of electric vehicle simulation model.
2.3.2 Vehicle Parameters-Block 2 There are many parameters that need to be taking into account during simulation. In order
to evaluate the performance of the vehicle under different modes of operation and to
compare performance with different vehicle types, a standard basis for comparison is
required. For vehicle benchmarking, this usually the vehicle driving cycle which contains
typical road velocity profiles and gradient data, thus defining the various dynamic
conditions, i.e. acceleration, deceleration, and speed. Some driving cycles simulate urban
driving cycle and other cycles are used to simulate out of city or motorway driving. In this
model, a number of driving cycles are included in the main model library in Block 2 as
well as gradient profiles for each driving cycle, example of which are illustrated in Fig.
2.2.
42
(a) Velocity driving cycle options. (b) Gradient options for each driving cycle.
Fig. 2.2 Velocity and gradient profiles for some driving cycles.
2.3.3 Vehicle road load and traction forces-Block 2. A simplified model of the road vehicle kinematics can be used to estimate the dynamic
tractive requirement of the vehicle drive-train, from which the individual component
specifications can be rated with-regard-to their peak and continuous duties. The vehicle
model accounts for the resultant forces acting against the vehicle when starting and when
in motion, as illustrated in Fig.2.3.
selected D. C.1
SEVNewc
SEV290808
NEDCnorm
NEDCenh
NEDC DESERVEMultiportSwitch
JAP11
HFET
FTP75
Edison
ECE15norm
ECE15enh
ECE15 DESERVE
SELECTED No.1
THETA 1
theta of SEVNewc1
theta of SEVNewc
theta of NEDCnorm
0
theta of NEDCenh
0
theta of HFET
0
theta of FTP75
0
theta of Edison
theta of ECE15norm
0
theta of ECE15deserve
0
theta of JAP11
0
theta od NEDCdeserve
0
MultiportSwitch
Divide
theta of ECE15enh
0
DC selection2
Selection No.1
43
Fig. 2.3 Forces acting against the vehicle when starting and when in motion.
These forces can generally be considered as comprising of four main components:
• the force to overcome the tyre to road power loss, or rolling resistance,
θcosgmKF rr =
• the resistive force related to the road gradient, θsingm ,
• an aerodynamic resistance or drag force, 2
2
1vACF fda ρ= , and
• the transient force required to accelerate or retard the vehicle, dt
dvm .
The above components forces are summed to yield the total force required at the vehicle
wheels expressed as a function of the vehicle linear motion, i.e.:
dt
dvmFmgFF ard +++= θsin (2.1)
where: rK is the rolling resistance coefficient which includes tyre loss and is
approximated to be independent of speed and proportional to the vehicle normal reaction
force, m is the vehicle and payload mass, θ is the road gradient, g is the gravitational
constant, ρ is the density of the air, dC is the drag force coefficient, fA is the vehicle
frontal area, and v is the vehicle linear velocity.
Having determined the forces acting upon the vehicle, the road wheel torque can be
calculated from the equation of the motion:
dwf
www Frd
dt
dJT ⋅⋅+⋅=
ω
(2.2)
where: wJ , wω , wr are the wheel inertia, angular velocity and mean radius, respectively,
and fd is a distribution factor proportioning torque distribution on the rear axle. By way
FrFr
44
of example, for a direct rear wheel drive scenario where it is assumed that there is an equal
share of the required tractive force between each rear wheel drive machine fd =0.5. For a
single on-board drive machine option df =1.0. If a gear stage is included in the drive-train,
the output torque of the traction machine is related to the road wheel torque by the total
transmission gear ratio, nt transmission efficiency, ηt, and the machine rotor inertia, mJ .
Here, the total gear ratio and efficiency include the contribution of the differential if
included. Incorporating these components into the equation of motion yields a general
expression for traction machine torque:
w
tt
mmm T
ndt
dJT
ηω 1+= (2.3)
Expressing the wheel and traction machine angular velocities in terms of the vehicle linear
velocity yields:
ww r
v=ω (2.4)
wtm r
vn=ω (2.5)
From which the machine torque equation can be expressed in terms of the vehicle linear
velocity by substituting equations (2.1), (2.2), (2.4), and (2.5) into equation (2.3):
f wt t w
m w t t w t t
d r mn J d vT r n r n d t
ηη η
+ + =
2( cos sin ) 0.5f w
r d ft t
d rk mg C A v
nθ θ ρ
η + + +
(2.6)
The parameters included in the equation depend on the vehicle type. In the model, the
vehicle library contains a number of vehicles options with their parameters. The
mechanical power needed to satisfy the prescribed vehicle velocity driving cycle is the
torque multiplied by the mechanical speed:
mmm TP ω.= (2.7)
2.3.4 Traction machine model-Block1. To find the total electrical power that taken from the energy system of the vehicle, it is
necessary to include all component losses in the power-train. In this model, the efficiency
45
of the gear system and electrical machine are lumped together since this is as presented by
vehicle test data available from DESERVE. There are therefore three options available in
the model:
• a library of traction machine efficiency is built in the model as a 2-D array,
• a simple fixed efficiency option,
• a traction machine, inverter and gear stage specific efficiency map in 2-D array. The
machine operates as a motor in acceleration mode or as a generator in regenerative
mode, thus the energy flow considered and calculated from the efficiency map. An
example of the efficiency map. The efficiency map Look-up table as implemented in
the model is illustrated in Fig. 2.4.
According to the losses in the electrical machine, the electrical power input to the machine
inverter is greater than the mechanical power in motoring case and less than the
mechanical power in regenerating case. In Fig. 2.4, the positive torque Tpve (motoring case)
and negative Tnve (regenerating case) are differentiated as illustrated by equations (2.8) and
(2.9) respectively.
ηm
electric
PP =
(2.8)
η.melectric PP = (2.9)
where Pelectric is the demand electric power from traction machine, and η the specified
conversion efficiency at particular operating point to calculate the losses. Considering the
mechanical power requirements and traction machine, inverter and transmission losses, the
total traction power is added to other electrical system loads, such as auxiliaries etc., thus
calculating the total power required from the on-board energy systems:
loadssauxiliariemdemand PPP += (2.10)
LookupTable (2-D)
Power
-K-
Tnve
Tpve
Speed
Torque
Abs
|u|
%
100
Fig. 2.4 The efficiency map as a 2-D Look-up table in the model.
46
2.3.5 ZEBRA Battery model
In electric vehicles, the battery model as the main energy source is a very important issue
on which to focus. As previously stated the main energy source that has been the focus in
this thesis is the ZEBRA battery. Many researchers have published papers describing a
wide range of battery models [73]. The battery is a key element in the energy source
system of electric vehicles; hence an accurate battery model is a very important for a
vehicle model as a whole to improve the total system energy efficiency predictions. The
battery model must be robust and model the battery chemical phenomena such as the
diffusion effects, ohmic resistance, self discharging and mass transport limitations so as to
predict accurate battery voltage, current, and state-of-charge (SOC). There are several
battery models, reported in the literature, aimed to reflect the battery characteristics. The
simplest battery model is shown in Fig. 2.5(a) consisting of an ideal voltage source (EO)
and a constant equivalent internal series resistance (ESR) [73, 74].
The model does not account for the variation of open-circuit voltage and resistance with
the variation of SOC and charge/discharge current. This model is widely used where the
energy of the battery is assumed to be unlimited and is hence not suitable in this study
because the energy calculation is very important. This simple model is improved and
becomes more dynamic if the non-linear characteristics of both open-circuit voltage and
internal resistance are considered [75].
The authors in [76, 77] described the equivalent circuit model, shown in Fig 2.5(b), as a
dynamic model where the parameters vary with SOC, temperature and direction of current.
In ADVISOR this model is called the internal resistance model and offers parameter for
many types of batteries except the ZEBRA. Moreover, the ESR SOC functions are limited
to 3 or 4 characteristics which subsequently limit simulation resolution. Also, voltage
predictions in the model show a maximum error of 12%, while energy prediction accuracy
is not stated.
Some authors present models that include capacitive element to represent dynamic battery
plate characteristics and to get smooth responses across the terminals [76]. Thevenin type
battery models are commonly used as shown in Fig. 2.5(c). All parameters including open-
circuit voltage EOC, internal resistance R, the overvoltage resistance RO, and the
capacitance of the parallel plates CO are constant. This is a disadvantage because the
parameters should vary with the battery condition.
47
The resistance-capacitance model and Partnership for a New Generation Vehicle (PNGV)
model also use resistive capacitive combinations, as described in [76, 77] and are shown in
Fig. 2.5(d) and (e) respectively.
ESR= f (SOC)
EOC= f (SOC) + -
(a) Simple equivalent circuit
battery model. (b) More dynamic battery equivalent circuit model.
RO
R
EOC
CO
Rt= f (T,SOC) Re= f (T,SOC)
Rc= f (T,SOC)
Cc= f (T) Cb= f (T)
Ro C=1/Voc
Cc= τ/RP RP
(c) Thevenin battery model. (d) The resistance-capacitance model.
(e) PNGV model including capacitance.
Rd
EP Es
Rs
Rp
Cd
Cw
Rw
(f) Forth-order dynamic model. (g) Three branches battery model.
Fig. 2.5 Several battery models.
Another fourth-order dynamic model is proposed in the literature [79]. Here, the model
contains capacitance, resistance and two emf source elements, as shown in Fig. 2.5(f). The
RC branches describe ohmic effect Rd and Cd, waste energy is modelled by Rw and Cw,
while resistors Rd and Rs account for electrolyte reaction and self discharge respectively.
The model is complicated by the significant use of empirical data.
The authors in [79] convert the series equivalent circuit to parallel branches to describe the
charge and discharge efficiency over a wide frequency range. They state that battery
efficiency does not depend on current levels, but varies greatly with frequency. The model
is in three branches as shown in Fig. 2.5(g), but it could be simplified to one or two lumped
braches. It is stated in [79] that the model is not capable of accounting for the full
Rc Rb
Cb Ca
Ra
Cc
48
frequency range of interest. Note, for typical EV systems the battery DC link current has a
very low power dynamic, typically less than 100Hz.
Finally, other approaches to battery modelling use computational intelligence techniques
such as artificial neural networks (ANN) to design a non-linear function for the battery
parameters. The tool is based on training data, so the model will be only an accurate over
the training range [76].
As mentioned, the ADVISOR software offers five battery models with validation date for
lead-acid and NiMH batteries [76], the ZEBRA battery is not included. In [80], the author
illustrated an equivalent circuit model of the ZEBRA battery to explore the electro-
chemical behaviour of the cell. The model is based on how the parameters are determined.
It found out that for example; the iron cell resistance is estimated based on nickel
resistance. However, actual nickel resistance is difficult with an increased Depth-of-
Discharge DOD.
Instead of modelling detailed chemical properties, the battery model used in this thesis is
based on electrical terminal data provided as part of the DESERVE program. The model
implements a detailed non-linear open-circuit emf characteristic that is only a function of
state of charge (SOC) and both discharge and charge resistances that are complicated
functions of SOC and the magnitude of charge/discharge current. Test were specified and
to Beta R&D who then carried at the test regime, collected and supplied the test data [82].
2.3.6 Battery state-of-charge (SOC)
The battery state-of-charge (SOC) describes the available capacity in relation to the
nominal capacity of the battery. SOC is the relative remaining stored charge (Ah) in the
battery and is usually expressed in percentage or per unit [83, 84]. The battery SOC is the
key quantity as it is a measure of the amount of electrical energy stored in or released from
the battery during charging or discharging respectively, effectively equating to a fuel gauge
in conventional vehicle SOC is calculated from:
total
actual
Q
QSOC=
(2.11)
where: Qactual is actual battery charge, and Qtotal is total battery charge.
This parameter is very important for any a successful energy management scheme, hence
an accurate calculation or measurement is required for SOC. Many techniques have been
49
used for SOC calculation, the author in [85] determine SOC using fuzzy logic techniques,
while others use Peukert’s as in [86], and neural network as in [87]. Since the ZEBRA
technology is 100% columbic efficient (as discussed in Chapter 1), SOC can be accurately
measured by measuring and recording (data logging) amperes in, and out of the battery [5].
In most SOC calculations, the charge/discharge current is integrated over time and related
to battery nominal capacity. This study considers that the available capacity of the battery
changes as a function of charge/discharge current. Hence, SOC is tracked according to the
battery terminal current and calculated via:
3600
.'
Ah
dtISOCSOC ∫−
=
(2.12)
where SOC’ is the initial state-of-charge, I the charge/discharge current, and Ah the
capacity of the battery (Ampere-hour).
2.4 The ZEBRA Battery Model
The ZEBRA battery is modelled in the Matlab/Simulink environment using the look-up
table technique populated with experimental test data. The model is used to assess the
battery dynamics and to understand the interconnection issues in terms of energy flow,
circuit voltages and current transients. An overview of the ZEBRA battery model is
illustrated in Fig. 2.6, and the model execution process described in the flowchart of Fig.
2.7.
Fig. 2.8 illustrates the ZEBRA model in Simulink environment, showing a Look-up table
to calculate the internal resistance as function of SOC and current, and Lookup table to
calculate the open-circuit voltage as a function of SOC. The results of the Look-up table
for the internal resistance and the open-circuit voltage are illustrated in Fig. 2.9 and Fig.
2.10, respectively. Typical output results from the ZEBRA model in terms of power loss,
internal resistance, state-of- charge, open-circuit and terminal voltage when the battery is
discharged over NEDC driving cycles are illustrated in Fig. 2.11.
50
Fig. 2.6 The layout of ZEBRA battery model.
Fig. 2.7 The flowchart of the model process.
Current demand
Discharging
charge ( disq )
Idtqdis ∫=
Rint Look-up table
Eoc Look-up table
The voltage drop on Rint
State of Charge (SOC)
The voltage of the battery is calculated
The result
Power profile (input data)
Power
demand
SOC
SOC & Current
Calculations
I
VOC
Look up table
Rint Look up table
+ -
51
Voltage drop
3
Terminal Voltage
2
Eoc
1
change to ohms
0.001Rint=f(SOC, I) as Look up Table
Eoc=f (SOC) as Look up Table
Current (I)
3
No. of cells
2
SOC
1
Fig. 2.8 ZEBRA model in Simulink environment.
Fig. 2.9 The results of Look-up table for the internal resistance.
2.3
2.35
2.4
2.45
2.5
2.55
2.6
2.65
2.7
00.20.40.60.81
SOC
Eoc/
V .
Fig. 2.10 The results of Look-up table for open circuit voltage.
52
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-50
0
50
100
Time (s)
Current (A)
(a) Battery current.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000
1
2
3
4
5
6
7
8
9
Time (s)
Internal Resistance (ohm
s)
(b) Battery internal resistance.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000500
510
520
530
540
550
560
570
580
Open Circuit Voltage (V)
(c) Battery open circuit voltage.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000300
350
400
450
500
550
600
Time (s)
(d) Terminal voltage.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SOC
(e) Battery state-of-charge.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000
1000
2000
3000
4000
5000
6000
7000
8000
9000
(f) Internal power loss.
Fig. 2.11 ZEBRA model over repetitive NEDC driving cycle.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
1 0
SOC
Vol
tage
(V
)
Vol
tage
(V
)
Res
ista
nce
(mΩ
)
Cur
rent
(A
)
Pow
er L
oss
(kW
)
8 4 0
600 300
600 300
9 0
100 50 0 -50
53
For a typical ZEBRA Battery System Design, the ZEBRA battery has battery management
interface (BMI) that can control the ohmic heater (to raise the battery temperature to
280oC) and a cooling fan that force ventilates the battery during high rate charge/discharge.
The BMI also controls the main circuit contactors that connect the positive and negative
poles of the battery [34]. Therefore, the battery limits voltage and current transients by
opening the main control contactors, for example, when the battery terminal voltage
exceeds the maximum voltage during regenerative braking, or the battery is fully charged,
or when the battery voltage drops below the minimum level during high acceleration, or
when near zero SOC. Thus, contactors are also included in the model via a block that
implements management interface (BMI). The battery management interface unit controls
the voltage limits by disconnecting the battery using electromagnetic contactors. Moreover,
other operating limits are implemented in the model, such as the charging and discharging
current and state-of-charge limits to avoid deep discharge of the battery [35]. In large
vehicle applications, a number of high-energy traction batteries may be connected in
parallel are to obtain the required energy and power. In this multi-battery system, an
additional controller is needed to supervise the BMIs of each battery, this controller is
called the multi-battery server, or MBS. The MBS supervise the individual batteries in the
system via their respective BMIs and the vehicle system. This management unit will be
discussed in Chapter 3, which investigates the problems of unbalance in multiple battery
systems.
2.5 Vehicle Model Validation
The vehicle model was simulated and results generated based on a known vehicle driving
cycle taken from test. To validate the model the model results experimental measurements
were taken from one of the smith Electric Vehicle’s (SEV’s), Edison Van over an
approved SEV driving cycle as part of the DESERVE project. The Edison Van model
parameters are detailed in Table 2.1. The Edison Van is powered via two ZEBRA batteries
(2x76 Ah), each with an initial state-of-charge (SOC) of 92%.
The analysis was undertaken for three tests:
(i) using a measured DC link power profile as a demand input to the battery system
simulation model,
(ii) using vehicle measured speed and gradient profiles and employing a fixed
efficiency for the traction machine and inverter, and
(iii) using vehicle measured speed and gradient profiles and employing an efficiency
map for the traction machine and inverters.
54
For test (i) the measured DC link power profile used as a demand input to the simulation is
illustrated in Fig.2.12. This power profile is actual road test data provided via the
DESERVE project [21]. The calculated and measured SOC and a time-zoomed example of
terminal voltage are shown in Fig. 2.13 and 2.14 respectively, showing good agreement.
The difference in end-SOC is 2% from measured and there is a ± 3.7% variation in
predicted terminal voltage. The energy used during this test was 33.69kWh, thus a range of
64.42km was calculated from the associated velocity profile.
Equation (2.6)
parameters Description Value Units
m Mass during test 3140 kg Cd Co-efficient of drag 0.4 - Af Frontal area 4 m2 Kr Co-efficient of rolling resistance 0.016 - nt Gear and deferential ratio 11.95:1 - rw Tyre radius 0.364 m ηt Machine efficiency 0.85 p.u.
Other model parameters
Ratio of regenerative to brake energy 0.75 - Controller efficiency 0.9 p.u. Final gearing efficiency 0.85 p.u. ZEBRA battery capacity 76 Ah (each) Number of batteries 2 - Nominal voltage 280 V Inverter-Max output current 350 A Inverter-Max regenerative current 150 A
Table 2.1 Edison Van data.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-40
-30
-20
-10
0
10
20
30
40
50
60
Time (s)
Pow
er (kW
)
Fig. 2.12 The measured power time profile for the SEV Edison Van used for case (i) of the validation study.
55
0 2000 4000 6000 8000 10000 120000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SO
C
SimulatedMeasured
Fig. 2.13 The calculated and measured SOC.
100 200 300 400 500 600 700 800 900 1000150
200
250
300
350
Time (s)
Volta
ge (V)
SimulatedMeasured
Fig. 2.14 The calculated and measured terminal voltage.
56
Case (ii) and (iii) include the Edison Van vehicle kinematics and equations (2.6 to 2.7) to
calculate the required power over the measured velocity and gradient profiles. Again, this
data was provided via the DESERVE project [5], shown in Fig. 2.15. The velocity driving
cycle is added to the driving cycle library Block 2 and the gradient profile added to
gradient library Block 2. The mechanical torque is calculated and thus the required
mechanical power. For case (ii) a fixed efficiency traction system is used to calculate the
required electrical power from the battery system, while in case (iii), the traction system
efficiency map explained earlier is used, both elements being options in Block 1.
Results for case (ii), the fixed efficiency drive system, are illustrated in Fig. 2.16, showing
the calculated SOC. The end SOC is greater than actual SOC by 12%. It is clear that the
energy used is less than the previous test, the energy used in this test for driving the van for
64.42km is 30.64 kWh because of the mode termination due to minimum voltage. Fig.
2.17 shows an example of the simulated and measured terminal voltages showing a
difference of 15V. Case (iii) considered an efficiency map implementation for the drive
system. The final SOC’s are different by 20% as shown in Fig. 2.18 (a), because of that
and as expected, the energy used for the same distance is less which 26.64 kWh. The error
of the SOC difference is shown in Fig. 2.18 (b). The voltage difference is now worse than
for case (ii) at 20V because the efficiency is worse than for the fixed efficiency case which
leads to more power required from battery system; Fig. 2.19 compares measured and
predicted terminal voltage. From the three tests it can be concluded that less energy or an
underestimate of energy is made when using a fixed efficiency drive system and efficiency
map drive system a opposed to the actual measured power profile as the input. To address
these uncertainties further tests were requested from the DESERVE consortium and
simulations undertaken to determine the sensitivity of results to the various model
parameters.
57
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000
5
10
15
20
25
30
Time (s)
Spee
d (m
/s)
(a) Velocity-time data
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
Time (s)
grai
den
t (d
egre
e)
(b) Gradient-time data
Fig. 2.15 Measured test data from the Edison Van.
58
0 2000 4000 6000 8000 10000 120000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SO
C
SimulatedMeasured
Fig. 2.16 SOC results for case (ii) the fixed efficiency drive system test before calibration.
100 200 300 400 500 600 700 800 900 1000150
200
250
300
350
Time (s)
Volta
ge (V)
SimulatedMeasured
Fig. 2.17 Terminal voltage results for case (ii) the fixed efficiency drive system test before calibration .
59
0 2000 4000 6000 8000 10000 120000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SO
C
SimulatedMeasured
(a) Test result of SOC.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000
0.05
0.1
0.15
0.2
0.25
Time (s)
SO
C d
iffe
ren
ce
(b) Difference in simulated and measured SOC.
Fig. 2.18 SOC results for case (iii) the efficiency map drive system test.
60
100 200 300 400 500 600 700 800 900 1000150
200
250
300
350
Time (s)
Volta
ge (V)
SimulatedMeasured
Fig. 2.19 Terminal voltage results for case (iii) the efficiency map test.
2.6 Model Calibration
2.6.1 Acceleration Limits During the validation test using the Edison Van driving cycle, the peak acceleration
calculated from the measured vehicle velocity data was 3.4m/s2, which is too high, being
representative of high performance cars such as the Jaguar XJ6 (4.5m/s2) and the Aston
Martin (6.5m/s2) [74]. The calculated vehicle acceleration is shown in Fig. 2.20. There are
many techniques that could be used to calibrate the measurements, such as scaling the
original data profile, or cutting off any data points above the highest acceleration possible
for the Edison Van used in this research. The author feels that scaling the original data
profile is inappropriate as it results in mitigating parts of the measured realistic speed
profile, and therefore the cut off of the unexpected and unrealistic peaks that are artefacts
of measurements is chosen for data analysis. After discussions with SEV and inspection of
their test data, it becomes clear that there was noise or other errors on the actual measured
velocity test data Thus, given the extent of the measured DC link power data, a speed
limiter was used in the model, as shown in Fig 2.21, to limit the acceleration to 0.75 m/s2
which, after discussion with SEV, was deemed acceptable for a 3.5 tonne van. The
calibrated and the measured acceleration results are now shown in Fig. 2.22
61
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-4
-3
-2
-1
0
1
2
3
4
Time (s)
3200 3250 3300 3350 3400 3450 3500-3
-2
-1
0
1
2
3
4
Time (s)
Acc
eler
atio
n (m
/s)
Simulated acceleration
Fig. 2.20 Edison Van acceleration calculated from the vehicle measured velocity data.
Fig. 2.21 Acceleration limiter to correct measurement.
A
ccel
era
tion
(m/s2 )
A
ccel
era
tion
(m/s2 )
Time (s)
Time (s)
Limiter
Torque Limiter
62
3430 3440 3450 3460 3470 3480 3490 3500-2
-1
0
1
2
3
4
Time (s)
Calibrated Measured
Fig. 2.22 Calibration of vehicle acceleration.
2.6.2 Braking Torque Limits The actual torque is constrained by the vehicle drive system limits. In practice, the braking
torque cannot be simulated because the drive system strategy is unknown. So to justify
and validate the model, the regenerative torque is analysed to calibrate the model by setting
the matched braking torque of the real drive system. To analyze the torque calculation, the
following analysis measures the impact of the regenerative energy on the calculation result.
The first step in this test is to investigate the effect of the regenerative torque on the range
and energy used. Table 2.2 and Fig. 2.23 show that when regerative torque was not taking
into account and limited to zero, all the battery energy is discharged in a distance shorter
than expected. In the case of using regerative torque, less energy was used resulting in
greater distance, which is thus over estimated. Hence, minimum regenerative torque will
be used and a set point chosen to ensure that the energy calculation agrees with actual.
In Table 2.3, the limiter on the torque is set to different regenerative torque values. For no
regenerative torque, the energy used could not establish the expected range. If the
regenerative torque is increased gradually, more energy is gained, but the minimum
voltage terminated the model at range of 60.88km, as shown in Fig. 2.24.
To justify the measured and simulated energy use, the minimum voltage of the battery is
set to 1.5V/cell. Table 2.4 and Fig. 2.25 show results for regenerative torques ranging
A
ccel
erat
ion
(m/s2 )
Time (s)
63
from 0 to 75 Nm. It is clear from Fig. 2.25, that the measured SOC matches the simulated
SOC when the regenerative torque is 35Nm, as shown in Fig. 2.26. On application of this
calibration, the simulated and measured SOC of a fixed drive-train, as well as efficiency
map drive-train, show a good agreement, as illustrated in Fig. 2.27. The terminal voltage
of the calibrated and measured data is shown in Fig. 2.28.
Test Input energy
(Ein) Output energy (Eout)
Net energy used
SOC Range (km)
Power profile 5.93 33.42 27.49 0.13 64.42 With regen. 8.87 30.64 21.78 0.216 67.66
Without regen. 0 29.32 29.32 0.1 60.75
Table 2.2 Test of energy used.
0 20 00 4 000 6 000 800 0 1000 0 120 000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time /s
SO
C
measured soccalculated soc witho ut regen.calculated soc with regen.
Fig. 2.23 SOC when regenerative torque limited to zero (no regen.).
Regen. torque limit set-point
Ein Eout SOC Range
0 0 29.2 0.1 60.75 -5 0.28 28.89 0.115 60.88 -10 0.85 28.86 0.131 60.88 -15 1.4 28.85 0.145 60.88 -20 1.9 28.4 0.158 60.88 -30 2.8 28.84 0.183 60.88 -35 3.27 28.84 0.193 60.88
Table 2.3 The regenerative torque is set to different limits.
Simulated with Regen. Measured SOC Simulated without Regen.
64
0 2000 4000 6000 8000 10000 120000
0.2
0.4
0.6
0.8
1
Time (s)
SOC
soc at regen.T=0measured socsoc at max. regen. T = -5 soc at max. regen.T = -10 soc at max. regen. T=-15soc at max. regen.T=-20soc at max. regen. T=-30soc at max. regen.T=-35
Fig. 2.24 SOC at different regenerative torque set-points.
Regen. torque
set-point Ein Eout SOC Range
0 0 29.2 0.1 60.75 -5 0.29 29.4 0.1 62.36 -10 0.88 29.9 0.1 63.25 -15 1.45 30.37 0.1 63.93 -20 1.99 30.66 0.105 64.42 -30 2.97 30.65 0.13 64.42 -35 3.4 30.65 0.14 64.42 -40 3.83 30.65 0.151 64.42 -60 5.2 30.64 0.18 64.42 -75 5.9 30.64 0.2 64.42
Table 2.4 Regen. torque set-point vs. Energy used, Vcellmin = 1.5V.
65
0 2000 4000 6000 8000 10000 120000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SOC
Measurement socSOC at max. regen. T=-75SOC at max. regen. T=-60SOC at max. regen. T=-40SOC at max. regen. T=-30SOC at max. regen. T=-20SOC at max. regen. T=-15SOC at zero regen.
Fig. 2.25 Results of regenerative torque set-points from 0 to 75 Nm.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 110000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SOC
Calculated socMeasured soc
Fig. 2.26 SOC when the regerative torque set-point is 35Nm. .
66
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SOC
Simulated SOCMeasured SOC
Fig. 2.27 SOC for a fixed efficiency drive system after calibration.
0 100 200 300 400 500 600 700 800 900 1000150
200
250
300
350
Time (s)
Vol
tage
(V)
Simulated Measured
Fig. 2.28 Example of terminal voltage variations for fixed
efficiency drive system after calibration.
67
2.7 Model Sensitivity
The developed ZEBRA battery model uses a look-up table of open-circuit emf versus
battery SOC and a look-up table of internal resistances versus SOC and charge-discharge
current. To test the sensitivity of the model to resistance data, the look-up table is replaced
by a constant resistance that is varied from 4 to 12 mΩ. The model is then tested for three
cases; (i) the measured power profile, (ii) the measured velocity profile with a fixed
efficiency drive system, and (iii) the measured velocity profile with an efficiency map
characterised drive system.
In the first case (i), as the internal resistance is increasing, the power (energy) loss is
increasing, thus more energy is used, and vice versa, as the internal resistance is decreased
less energy is used for the same distance, as shown in Table 2.5 and Fig. 2.29. As a result,
if the internal resistances of look-up table are averaged, the calculated SOC shows a good
comparison with the measurement SOC. The internal resistance is a function of
discharging current and SOC, so different current demand gives different internal
resistance. In other words, if the model uses a fixed internal resistance, this value should
be changed as driving cycle changed. This is a weakness of other battery models for
example those available in ADVOSOR.
The second case (ii) is to test a fixed (single-value) efficiency drive system in the model
having a different fixed resistance instead of look-up table resistance. The results are
illustrated in Table 2.6 and Fig. 2.30.
The third case (iii) is to test an efficiency map drive system in the model having a different
fixed resistance instead of look-up table resistance. The results are illustrated in Table 2.7
and Fig. 2.31.
It can be concluded from the results of the last two cases that as the battery internal
resistance is decreased (which equates to less energy loss) the SOC difference between the
measured and simulated is greater. On the other hand, when the resistance is increased
(more energy loss), the SOC difference is decreased, and the terminal voltage goes under
the threshold of battery minimum voltage, which terminates the simulation.
68
Internal resistance (mΩ) SOC Distance (km)
12 0.27 49.47 10 0.124 64.42 8.5 0.154 64.42 8 0.163 64.42 6 0.194 64.42 4 0.22 64.42
Look-up table 0.132 64.42
Table 2.5 SOC for different battery internal resistance using power profile.
0 2000 4000 6000 8000 10000 120000.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time/s
SOC
Rin=4Rin=6Rin=8Rin=8.5look-up tableRin=10Rin=12
Fig. 2.29 SOC for different battery internal resistance.
Internal resistance
(mΩ) SOC Distance (km)
8.5 0.86 3.27 8 0.3 64.42 6 0.33 64.42 4 0.36 64.42
Look-up table 0.27 64.42
Table 2.6 SOC for different battery internal resistance
using a fixed efficiency drive system.
69
0 2000 4000 6000 8000 10000 120000.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s)
SO
C
Rin=6Rin=8Rin=4Look-up tableRin=8.5
Fig. 2.30 SOC for different battery internal resistance
using a fixed efficiency drive system.
Internal resistance
(mΩ) SOC Distance (km)
8.5 0.437 64.42 8 0.44 64.42 6 0.46 64.42 4 0.48 64.42
Look-up table 64.42
Table 2.7 SOC for different internal resistance using efficiency map drive system.
0 2000 4000 6000 8000 10000 120000.4
0.5
0.6
0.7
0.8
0.9
1
Time/s
SO
C
Rin=8.5Rin=8Rin=6Rin=4Look-up table
Fig. 2.31 SOC for different internal resistance using efficiency map drive system.
70
2.8 Summary
The proposed electric vehicle simulation model has been described in detail. The model
integrates separate vehicle component models into a complete vehicle drive system model
that will approximate the behaviour of the system of interest. The vehicle model will show
how component choices affect the overall performance of the system beyond the particular
aspect that motivated the selection. The system model suggests a set of combinations by
selecting, sizing and configuring the components, and then allows graphical optimisation
of the control scheme to realise the target performance requirements. The model will be
used to investigate proposed combinations of energy and power dense sources in detail in
subsequent Chapters of this thesis.
The energy dense source is specified and operated to fulfil the requirements for vehicle
range, while the power dense source provides the peak power for acceleration or collecting
regenerative braking energy, thus alleviating the peak power requirements of the energy
dense source. There are many options of energy source combinations for electric vehicles,
however, in this study the ZEBRA battery technology is chosen to be modelled as the
energy dense source since it, along with lithium-ion based technologies, shows the greatest
electro-chemical energy density to-date. Supercapacitors are modelled and simulated in
subsequent Chapters and an ICE and generator are modelled as a range extender unit for
sub-urban and inter-city travel in Chapter 5.
Instead of relying on detail characterisation of battery chemistry, the battery model is
modelled based on its electrical terminal behaviour, implementing a detailed non-linear
characteristic of both discharge and charging resistance to model performance. The model
is calibrated and validated by measured data provided by the DESERVE project.
The model sensitivity to battery internal resistance has been studied, where it has been
concluded that as the look-up table implementation of internal resistance showed an
excellent agreement with measured results.
71
CHAPTER 3
MULTI-BATTERY OPERATION
3.1 Introduction
In high energy/power vehicle applications, for example buses, lorries, off-road military
vehicles (tanks, armoured personnel carriers etc.), a number of batteries may have to be
connected in parallel to obtain the required energy levels or satisfy the peak power
demands, moreover to increase the fault tolerance capacity [88, 89].
The parallel connection makes the system venerable in the event of unbalance or faulted
operation that could occur at any time during the energy source lifetime. Unbalanced
operation occurs when the batteries are charged and discharged at different rates, or when
the respective battery cells are not equally charged. Long time operation with these small
cell differences can lead to a significant difference in SOC that could result a deep
discharge of some of the battery cells. Deep discharge affects the battery life and should
therefore be protected against. Another unbalanced operation could happen when the
batteries are not identical. In many electric vehicle systems, the on-board energy sources
are very well matched (electrically and chemically) because the systems are relatively low
volume. However, as automotive volumes increase, or battery share projects take off, the
matching of multiple parallel systems will become more difficult to manage.
Further, because the batteries are connected in parallel, the voltage of healthy and weak
batteries must be balanced, hence during the charging process the healthy batteries could
be overcharged possibility leading to a failure mode of the battery [90, 91].
72
Chin-Sien et al., demonstrated that by using DC-to-DC converters that allow full control of
each battery independently, some batteries can be effective isolated and operation
independent of individual battery SOC [92].
Han-Sik Ban et al. proposed an unbalance current control to keep the internal resistance of
all cells constant over the battery demand profile. This was done by inserting an external
resistance and calculating the change of internal resistance using a micro processor [93].
However, such a system is only suited to multiple battery systems of relatively small
energy. On larger (vehicle) systems, the inclusion of series resistance is not a sensible
option.
Regardless of other battery technologies, if the ceramic of the ZEBRA battery fails, the cell
fails to an electrical short circuit. This means the battery technology favours high numbers
of series cells, i.e. high voltage (200-1000Vdc) systems, that is becoming a pre-requisite
for larger electric vehicle systems. Thus, the battery system can tolerate a number of cell
failures and can continue to operate; a feature that makes the ZEBRA battery a very robust
solution.
Giorgio M. et al. studied a parallel connection of 8 ZEBRA batteries to power an electric
bus, Europolis, as reported in [4], the study demonstrated that the ZEBRA system provided
the request of 12 hours operation as a daily mission in Lyon city under some fault
conditions.
In this Chapter, the ZEBRA multi-battery system is modelled. The model shows the
interconnection between the ZEBRA batteries and shows how the batteries share current
for different operational cases: viz. (i) different SOC and (ii) number of cell failures.
3.2 Faulted cell in a ZEBRA battery
As mentioned, the ZEBRA cell typically fails to electrical short circuit. Chemically, nickel
powder and salt (NaCl) which is mixed with NaAlCl4 are used as the cathode material.
This salt liquefies at 154ºC. In the liquid state, it is conductive for Na+ ions and acts as
separator for electrons. During operation, the Beta-alumina ceramic, β-Al 2O3, which is a
brittle material may develop a crack. In this case, the liquid salt, NaAlCl4, reacts with
sodium resulting in salt and aluminium [34]:
AlNaClNaNaAlCl +⇒+ 434 (3.1)
In the case of small cracks, the salt and aluminium close the crack in the β-Al 2O3, however
for larger or progressive cracks, the aluminium shorts the path of current between the
73
positive and negative poles leading to that cell going to low resistance [34]. When the cell
fails, it can be gradual, depending on the current and other operating conditions, for
example a high discharge rate will soon take the cell down to complete failure. So a failing
cell does not take many cycles at all to stop contributing in the battery energy content.
In each sting of a ZEBRA battery, the limit on the number of allowed cell failures is the
reduction in energy content that can be accepted by the application, or in larger multiple
systems, the point when adjacent batteries start to significantly discharge into the failed
unit. The cell failure permissible is determined by the percentage of failed cells to the total
cells; which means higher voltage batteries can tolerate more cell failures. Generally, the
ZEBRA battery, has a tolerance of 5-10% of cell failures [34, 35, 94].
Each ZEBRA battery is fitted with a controller that keep the battery within the permitted
operating window in terms of SOC, maximum current, and voltages levels. In the parallel
connection, the controller manages the unbalanced system to maintain vehicle functionality
by disconnecting the weak battery in order to discharge other batteries to an equitable
level. In the charge mode, the battery cells could all polarise to the set charge voltage
because each battery has a separate charger which maintains the full charging voltage on
the batteries including those have or those don’t have cell failures. Then, a failed cell
would not be detected by an open circuit voltage test after charging unless the battery has a
rest or settling period of a few hours [95].
3.3 ZEBRA Multi-battery system
An example of a multi-battery system is illustrated in Fig. 3.1. Each individual battery
parameters are controlled by a Battery Management Interface (BMI). The battery is
connected to the system through a contactor or a circuit breaker. The specification of the
contactors should be that they can sustain a high current equitable to the full discharge
current [95].
In multi-battery systems, a Multi Battery Server (MBS), designed for up to 16 battery
packs, has to be connected in parallel to oversee the individual BMI’s[34]. The
communication between the MBIs and MBS manages the system operation by controlling
each battery contactors to connect or disconnect the battery from the system according to
the system requirements and battery health.
74
Fig. 3.1 A Multi-battery system.
3.3.1 Battery Management Unit (BMI)
The BMI is the intelligence of the battery system and consists of an electronics unit with
integrated main circuit breakers used to monitor and control the battery operation. A
typical Battery Management Interface (BMI) is illustrated in Fig. 3.2.
Fig. 3.2 ZEBRA Z5C Battery Management unit (BMI).
The BMI controls the battery internal heater and cooling fan to maintain the ZEBRA
battery operating at a suitable temperature. The BMI also acts to control the battery
operating limits, for example as in Fig. 3.3, when the battery terminal voltage exceeds the
maximum specified during regenerative braking or when the terminal voltage drops under
the minimum level during high rates of discharge, i.e. vehicle acceleration. The BMI unit
controls these extreme voltage limits by disconnecting the battery via contactors that
isolates the battery [34, 35]. The operating limits are implemented in the MBI to avoid
battery damage [94].
Battery management unit (BMI)
Circuit breaker
Multi-Battery Server
MBS
BMI
BMI
Contactors
Resistor
ZEBRA 1
ZEBRA 2
Load
75
Fig. 3.3 BMI Operation in a vehicle traction system.
3.3.2 Multi-Battery Server (MBS)
A controller is needed to supervise the BMIs of each battery and manage the operation of
the complete multi-battery system. This controller is called the multi-battery server
(MBS), and it operates as an interface between the individual battery systems via their
BMIs to the vehicle energy management system. MBS communicate with all BMIs on the
battery system and with the vehicle components.
Fig. 3.4 illustrates an overview of the multi-battery server in the vehicle energy
management hierarchy. As discussed, the ZEBRA battery could be operated with failed
cells, though with a reduced energy capacity and open circuit voltage. However, if parallel
batteries have a different number of failed cells, the terminal voltage of the batteries will be
unbalanced and hence their individual SOC will start to vary from ideal. The Simulink
model takes into account possible variations between batteries, and can be used to analyse
the performance of multiple battery systems in the case of various unbalanced operating
scenarios.
Fig. 3.4 Operational scheme of the MBS and BMI’s.
BMI
ZEBRA
Load
Idc Vdc
System voltage and current measurements
MBS Vehicle
control unit
BMI1
BMI2
BMI3
76
3.4 Multi-battery model
Each battery model has been simulated using a the Matlab/Simulink tool as previously
discussed. To connect multiple batteries in parallel, the two battery models are converted
to a Matlab “simpower” system. In addition, to all software connection of voltage sources
in parallel, small series resistances are required between the different voltage sources, else
the simulation solver cannot converge. The value of this resistance is in micro ohms which
is much smaller than smallest battery internal resistance of more than 1 milli-ohm. Thus
this resistance can be considered as cable resistance.
3.4.1 Model Layout
The model of four ZEBRA batteries in the Matlab/Smulink environment is illustrated in
Fig. 3.5, showing each battery connected to the battery system via two contactors, the
upper one is controlled by a control signal applied from BMI. The demand current that is
calculated in the Simulink model is converted to an electrical signal using a controlled
current source available from the Simpower tool box. The parameters of the four batteries
including SOC, number of strings, number of cells in series, and battery capacity in Ah are
set from the battery parameter block, as shown in Fig. 3.6. The BMI of each battery is
linked to the model using digital circuits to monitor battery voltage, current and SOC,
allowing the battery to operate between chosen operational limits, as shown by the scheme
of Fig. 3.7. MBIs are mastered by MBS to control the overall system operation.
3.4.2 The control scheme
The MBS manages the unbalanced system to maintain vehicle functionality by
disconnecting the faulty battery or the lower SOC battery in order to discharge other
batteries to an equitable level. The MBS algorithm is implemented in the model maintains
the SOC of each battery at within a variation of not more than 5% SOC as recommended
by Beta R&D (although this could be changed). If the vehicle power demand could be
provided without the faulty battery, the MBS will disconnect the faulty battery, and the
power will thus be developed from the remaining healthy batteries. However, the faulty
battery remains disconnected until the difference in SOCs is less than 5%. Nevertheless, if
the vehicle demand is high, the MBS keeps all batteries connected to supply the demand.
Fig. 3.8 illustrates a flow chart of the MBS algorithm.
77
Zebra Battery4
CR
NT
VT1
SO
C1
Con
n2
Co n
n1
Zebra Battery3CR
NT
VT1
SO
C1
Con
n2
Co n
n1
Zebra Battery2CR
NT
VT1
SO
C1
Con
n 2
Con
n1
Zebra Battery1CR
NT
VT1
SO
C1
Con
n 2 Con
n1
Voltages
Second Cotractor state
1
SOCs
MBS
Itotal
I4I3
I2I1
SOC4SOC3SOC2SOC1
s2
s1s4s3
Currents
i+
-
i+
-
i+
-
i+
-i+
-
Cotactors statess -
+
K-
g 12
g 12
g 12
g 12
g 12
g 12
g 12
g 12
Fig. 3.5 Layout of the multi-battery model.
initoal. SOC 1
1
initial SOC 4
-C-initial SOC 3
-C-
initial SOC 2
-C-
Time of the fault 2
0
Time of the fault 4
-C-Time of the fault 3
0
Time of the fault 1
-C-
No. faild cells 4
0No. faild cells 3
0
No. faild cells 1
0
No. faild cells 2
0
No. cells in series 4
108No. cells in series 3
108
No. cells in series 1
108
No. cells in series 2
108
NO. strings 4
2NO. strings 3
2
NO. strings 2
2
NO. strings 1
2
Ah 4
76Ah 3
76
Ah 2
76
Ah 1
76
BAT 4
int. SOC
NO. strings 4
Ah4
No. cells in series 4
No. faild cells4
time of fault
BAT 3
int. SOC
NO. strings3
Ah3
No. cells in series 3
No. faild cells3
time of fault
BAT 2
int. SOC
NO. strings2
Ah2
No. cells in series2
No. faild cells2
time of fault
BAT 1
int. SOC
NO. strings
Ah1
No. cells in series
No. faild cells
time of fault
Fig. 3.6 The battery parameters of four batteries in the multi-battery model.
78
Min. Voltage/cell
3SOC of Battery
2
BMI Signal1
AND
AND
OR
OR
AND
AND
AND
max. Votage/cell
> 3.1
> 0
<= 0
> 0
<= 3.1
AND
SOC min
>= 0.1
SOC max
<= 1.1
>=
<
boolean boolean
boolean
boolean
boolean
boolean
boolean
boolean
boolean
boolean
boolean
Current Limits
<= 224 |u|
min Voltage/cell5
No. of cells4
SOC3
Battery Current2
Battery Terminal Voltage1
Fig. 3.7 MBI model using digital circuits.
Fig. 3.8 Flow chart of MBS operation.
Yes
I
demand
Yes
N
BMI
SOC
SOC1 SOC2
…
SOC16
Disconnect the faulty battery
I
demand
<< 20 A
If the difference >5%
Connect/reconnect all batteries
79
The controller (MBS) signals are implemented in a Matlab function to satisfy the following
criteria:
(1) The battery is disconnected from the battery system if it’s SOC is less than
maximum SOC by 5% AND the demand current at that moment is less than 20A.
(2) The battery will be reconnected when the SOC becomes close to or equal to the
maximum SOC AND the demand current is less than 20A.
3.5 Model Validation
3.5.1 Field data analysis
Field measurement data of four ZEBRA batteries has been used to experimentally validate
the multi-battery model. In the first step of validation, the data is analysed and studied, as
illustrated by Fig. 3.9 showing the individual currents of four batteries. It is noticeable that
some currents equal zero at various times because the contactor of the relevant battery has
been opened by the MBS. Fig. 3.10 shows the voltages across each battery showing that
when the contactor is opened the voltage is battery open circuit voltage. From the SOC’s
illustrated in Fig. 3.11, it is clear to identify the switching states of the various contactors.
When the first battery SOC seems constant, it illustrates that that battery contactor is open
in response to the control signal from MBS.
Before analysing the measured data, it is worth pointing out that the individual battery
temperatures vary from 263 to 290 °C, which is not a significant variation for this
technology. Table 3.1 shows the starting data of SOC’s, terminal voltage, and
temperatures of four batteries. From the starting data, it clear that the second (No. 2)
battery has some failed cells. Although the cell failure is not clear from Fig.3.9 because
the demand current at starting time of the driving cycle is zero, Fig. 3.10 shows that the
voltage of the second battery is open circuit voltage by virtue of the open contactor.
During the driving cycle, two signals from the BMS switch off batteries 1 and 3.
Battery No. 1 2 3 4 SOC 99.9 99.9 99.9 99.9
Vt (V) 285.4 279 286.4 283.8 Temperature (C° ) 264.3 264.2 264.2 261.9
Table 3.1 Starting data of main parameters of the four batteries.
80
0 1000 2000 3000 4000 5000 6000 7000-60
-40
-20
0
20
40
60
80
100
120
Time (s)
Cu
rren
t (A
)
Ibat. 1I bat. 2I bat. 3Ibat. 4
Fig. 3.9 Current measurements of four parallel batteries exhibiting out of balance.
0 1000 2000 3000 4000 5000 6000 7000210
220
230
240
250
260
270
280
290
300
310
time (s)
Vol
tage
(V)
Vbat.1Vbat. 2Vbat. 3Vbat. 4
Fig. 3.10 Measured voltages across each battery.
0 1000 2000 3000 4000 5000 6000 70000
0.2
0.4
0.6
0.8
1
Time (s)
Sta
te O
f C
har
ge (S
OC
)
SOC1SOC2SOC3SOC4
Fig. 3.11 The SOC’s of the four batteries.
81
3.5.2 Model validation
For validation purposes, the model is run over the same drive cycle. The speed profile is
not provided in the given data; hence the currents of four batteries are summed to present
the demand current of the drive cycle, as shown in Fig. 3.12. Using the demand current as
an input to the four battery model, the MBS algorithm, which is based on the batteries’
SOC profiles, aims to control each of the four MBIs by sending a suitable command signal.
Each MBI responds by switching their respective contactors either ON or OFF. As a
result, the simulated contactor states for the four batteries are illustrated in Fig. 3.13.
In the test data, dynamics of the contactors has occurred at 3000 seconds, thus the result is
focused on this period of time to clarify the model result against the given test data. As an
example of the model and experimental results, two battery voltages are shown. The
voltage of battery 2, which had three failed cells at the beginning of the test, is shown in
Fig. 3.14. The applied MBS algorithm controls the MBI of battery 3 to switch the
contactors to the OFF position during the operation. The results of simulated and
experimental data for the voltage of battery 3 are shown in Fig. 3.15.
The contactor state data for the test MBS is not available, but the practical contactor states
can be inferred from the individual battery currents and SOC measurements. Thus, for
MBS validation, the responses of these measurements are compared with simulated results
by applying the proposed MBS algorithm. The results are illustrated in Fig. 3.16 showing
individual battery currents, SOC’s and contactor states, from which it is clear that the
model of the MBS controller matches the actual data with regard to disconnecting and
reconnecting battery 1. The result of the battery 2 contactor also shows good agreement.
The contactor is OFF at the beginning of the test but after 180 seconds it is reconnected.
Thus, the experimental data provides confidence in the model calibration.
Battery 3 is disconnecting early because both conditions are satisfied, in practice this event
could be referred to the delay of the contactors.
82
0 1000 2000 3000 4000 5000 6000 7000 8000-200
-100
0
100
200
300
400
Time (s)
Th
e d
eman
d c
urr
ent
(A)
Fig. 3.12 Demand current profile.
0 1000 2000 3000 4000 5000 6000 7000 8000
0
1
Time (s)
Co
nta
cto
r st
ate
Contctors state of Bat. 1Contactors of state Bat. 2Contactors state of Bat. 3Contactors state of Bat. 4
Fig. 3.13 Simulated contactor states of the four batteries
83
0 500 1000 1500 2000 2500 3000210
220
230
240
250
260
270
280
290
300
310
Time (s)
Vo
ltag
e (V
)
SimulatedMeasured
Fig. 3.14 Simulated and experimental voltage of battery 2.
0 500 1000 1500 2000 2500 3000210
220
230
240
250
260
270
280
290
300
310
Time (s)
Vo
ltag
e (V
)
SimulatedMeasured
Fig. 3.15 Simulated and experimental data of voltage of battery 3.
84
Fig. 3.16 Multi-battery model validation.
0 500 1000 1500 2000 2500 3000-60
-40
-20
0
20
40
60
80
100
120
Time (s)
Curr
ent (A
)
Ibat.1Ibat.2Ibat.3Ibat.4
0 500 1000 1500 2000 2500 30000.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Time (s)
SOC
SOC1SOC2SOC3SOC4
0 500 1000 1500 2000 2500 3000
OFF
ON
Time (s)
Con
tact
or s
tate
Contactor state of Bat. 1Contactor state of Bat. 2Contactor state of Bat. 3Contactor state of Bat. 4
CONT. 1 OFF
CONT. 2 ON CONT. 1 ON
CONT. 3 OFF
CONT. 1 ON
85
3.6 Model analysis
The reasonable agreement between simulated and measured data gave sufficient
confidence in the utility of the multi-battery simulation model to consider different fault
scenarios related to potential unbalanced operation. These are considered and studied in
this section to investigate the impact of each scenario on vehicle performance in terms of
energy and range.
3.6.1 Simulation of four batteries during various faulted operations
The vehicle of the validation data is analysed under different scenarios. The scenarios
considered in this section are:
(1) The four batteries have different SOC’s.
(2) The second scenario is a fault condition: when a fault occurs during driving the
vehicle, this case could occur in different batteries and with different numbers of
cell failure.
(3) The third scenario considers both scenarios (1) and (2) in one operation.
(4) The last scenario considers when progressive cell failures occur in one battery
during operation.
(1) Case one.
Practically, this case could happen when the batteries are at different ages or when one of
the batteries is damaged and replaced with other which has a different SOC and the vehicle
is driven straight away. In the model, the third battery is assumed to have less SOC (0.9)
than the other batteries which are fully charged as shown in Fig. 3.17.
86
1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0 7 0 0 0
0
0 .2
0 .4
0 .6
0 .8
1
T i m e ( s )
Swit
ch s
tate
S 1S 2
S 3S 4
0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0 7 0 0 00
0 . 1
0 . 2
0 . 3
0 . 4
0 . 5
0 . 6
0 . 7
0 . 8
0 . 9
1
T i m e (s )
SOC
S O C 1S O C 2S O C 3S O C 4
0 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0 7 0 0 02 0 0
2 1 0
2 2 0
2 3 0
2 4 0
2 5 0
2 6 0
2 7 0
2 8 0
2 9 0
3 0 0
T i m e ( S )
Vol
tage
(V
)
V 1V 2V 3V 4
Fig. 3.17 Results for case (1).
87
Table 3.2 shows the impact of different SOC of the third battery on the vehicle
performance; here the time spent in the journey represents the range.
SOC of 3rd battery
Time (s) SOC1 SOC2 SOC3 SOC4
1.0 6355 0.1 0.1 0.1 0.1 0.9 6221 0.1 0.1 0.1 0.1 0.8 6090 0.1 0.1 0.1 0.1 0.7 5882 0.1 0.1 0.1 0.1
Table 3.2 The impact of different SOC of the third battery on the vehicle performance.
(2) Case two.
The cell failure is simulated during driving. The fault might occur any time during driving,
this is very important on the system since the fault is simulated by a function of the number
of cells versus time. In this case, the fault time is considered as being at 3000s from the
beginning of the test. The number of failed cells that is implemented in this case is 5 cells
in each string (5 represent about 5% of cells in the battery) in the third battery. The case
results are illustrated in Fig. 3.18. The impact of failed cells on the vehicle performance is
shown in Table 3.3, where it is clear from that as more cells fail there is a greater impact
on range.
No. of cells/string
Time (s) SOC1 SOC2 SOC3 SOC4
0 6350 0.1 0.1 0.1 0.1 1 6327 0.1 0.1 0.1 0.1 3 6281 0.1 0.1 0.1 0.1 5 6229 0.1 0.1 0.1 0.1
Table 3.3 The impact of cell failure on the vehicle performance.
88
500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000
0
0.5
1
Time (s)
Switc
h st
ate
S1S2
S3S4
0 1000 2000 3000 4000 5000 60000
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1
Time (s)
SOC
SOC1SOC2SOC3SOC4
0 1000 2000 3000 4000 5000 6000180
200
220
240
260
280
300
Time (s)
Vol
tage
(V)
V1
V2
V3V4
Fig. 3.18 Results of case (2).
The cell failure is investigated for different cases for all batteries, as shown in Table 3.4
and Fig. 3.19, showing the impact of the vehicle performance in terms of range (operation
time).
89
No. of failed
cells in battery 1
No. of failed cells in battery
2
No. of failed cells in battery
3
No. of failed cells in battery
4
Time (s)
0 0 0 0 6350 0 0 0 1 6327 0 0 0 3 6281 0 0 0 5 6229 0 0 0 0 6350 0 0 1 1 6303 0 0 3 3 6206 0 0 5 5 6137 0 0 0 0 6350 0 1 1 1 6278 0 3 3 3 6127 0 5 5 5 5947 0 0 0 0 6350 1 1 1 1 6258 3 3 3 3 6048 5 5 5 5 5761
Table 3.4 Different cases of cell failure.
5700
5800
5900
6000
6100
6200
6300
6400
0 1 2 3 4 5
Number of faulted cells
Dri
vin
g ti
me
(s)
BAT3 BAT3+BAT4 BAT2+BAT3+BAT4 ALL BAT
Fig. 3.19 The impact of cell failure on the vehicle performance (or operation time).
(3) Case three.
This case considered a special case to present both different SOC and cell failure over the
driving cycle. At the beginning, the third battery has less SOC, and then at 3000 seconds
the battery succumbs to defects in 5 cells in each string. Fig. 3.20 illustrates the simulation
results showing battery contactor (switch) state, SOC and voltage.
90
500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000-0.1
0
0.5
1
1.1
Time (s)
Swit
ch s
tate
S1S2S3
S4
0 1000 2000 3000 4000 5000 60000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time (s )
SOC
SOC1
SOC2SOC3SOC4
0 1000 2000 3000 4000 5000 6000200
210
220
230
240
250
260
270
280
290
300
Time (s)
Vol
tgae
(V)
V1V2V3V4
Fig. 3.20 Simulation results for case (3).
91
It is clear that the switch of the third battery was OFF at the beginning of the test and then
reconnected after the SOC’s of all the batteries converge to the same state. The fault is
simulated to occur at time of 3000 s, so at this time the switch is opened again and remains
open until the SOC’s are approximately the same. Compared with the healthy system
operation which accomplished a driving for 6350 seconds, this case of operation could
accomplish 5850 seconds.
(4) Progressive cell failure.
When a cell fails in a battery, the remaining cells in the battery will be gradually
discharged depending on the discharge current; high discharge rates will soon take the
battery down to complete discharge. So a failing cell does not take many cycles at all to
stop contributing in the battery energy. Based on this fact, this case of failure is considered
where the maximum number of cells that fail progressively is about 10% of the total cells.
In this case, a model of progressive faults is implemented in the battery simulation. The
faults are gradual depending on the discharge current, hence the fault is simulated as a
function of discharge current rate. The results of this scenario, and its impact on the
vehicle performance, are illustrated in Table 3.5 and Fig. 3.21.
No. of failed cells Operation time (s)
0 6350
1 6314
2 6302
3 6279
4 6256
5 6231
Table 3.5 Progressive cell failures.
92
6220
6240
6260
6280
6300
6320
6340
6360
0 1 2 3 4 5
No. of faulted cells
Driv
ing
tim
e (s
)
Fig. 3.21 The impact of progressive cell failures on the vehicle performance.
3.6.2 Impact of cell failure on a two battery system - taxi performance
To investigate the impact of cell failure on the range of a known electric vehicle over the
NEDC driving cycle test, simulations of various percentages of cell failure were studied
the results of which are presented in Table 3.6. The reference vehicle is a London taxi
powered by two ZEBRA batteries over repetitive NEDC. The total distance that full
capacity batteries can establish is 93.97 km in the simulation model and previously
published data [17].
Battery 1 No. of failed cells in battery 2 Range (km)
0 93.97
2 91.95
Full capacity
5 86.5
Table 3.6 The impact of cell failure on vehicle range.
The first case of cell failure considered is when two cells fail in one battery; here it is clear
that the BMI controlled the damaged battery by switching the contactors OFF in order to
discharge the healthy battery up to the level of the damaged one. It can be noted that the
MBS kept the battery OFF when two conditions are valid;
93
(a) the SOC of the healthy battery is greater than the faulted battery, and
(b) the healthy battery can provide the load demand.
The range of the vehicle is slightly affected as shown in Table 3.6.
The second case is when the number of cell failures is the maximum allowed – i.e. five
cells. The performance of the vehicle is more affected than the first case, as would be
expected. The impact of the cell failure on vehicle range in the second case is that the
range is shorted by about 7 km.
3.7 Summary
Regardless to other batteries, the ZEBRA battery technology can operate with cell failures,
which tend to a short circuit due to the cell chemistry. This allows the string to continue
operation, a feature that makes the ZEBRA battery technology very robust. In this
Chapter, the multi-battery system has been modelled. The model shows the
interconnection between the ZEBRA batteries and illustrates how the batteries share
current for different faulted cases. The Simulink model takes into account the possible
variation between batteries and can be used to analyse the performance of multiple battery
systems in the case of various unbalance operating scenarios.
The MBS manages the unbalanced system to maintain vehicle functionality by
disconnecting the faulty battery or the lower SOC battery in order to discharge other
batteries to an equitable level.
Actual field test data of a four battery system has been used to validate the model. The
model simulations showed good agreement with the real test data. The model was hence
used to study different fault scenarios and to develop new fault management schemes.
94
CHAPTER 4
COMBINATION OF BATTERY AND SUPERCAPACITOR
4.1 Introduction
As previously discussed, some of the deficiencies of EV power-train technologies are the
high cost of energy storage batteries, and their limited peak power. Any improvement in
such points will make EV’s a stronger cost competitive solution against conventional ICE
vehicles. In pure EV’s, the battery is exposed to high pulse of power during acceleration
and deceleration. Due to this continuous supply of power peaks to the vehicle traction
drive, more stress is applied to battery than need be, consequently, the lifetime of electr-
chemical batteries is reduced [96-98]. Further, during these rapid acceleration and
deceleration events, the braking energy is inefficiently captured by electro-chemical
batteries because they generally have a relatively slow charge acceptance capabilities,
resulting in only 40-50% of potential braking energy being recovered back into the battery
[5]. Therefore, a peak power buffer in conjunction with the electro-chemical battery is
advantageous to store and release transient energy, thus improving the energy efficiency of
the vehicle power-train and, perhaps more importantly, to level battery energy [96-98].
This Chapter will review published work relevant to battery and supercapacitor
combinations in multiple energy source systems for electric vehicles and then review
published simulation models for supercapacitors. A Matlab/Simulink model will then be
95
presented, validated and subsequently used to design a supercapacitor power buffer for an
example urban electric vehicle.
4.2 Hybridisation of vehicle energy sources
The battery deficiencies mentioned here and in Chapters 1 and 2, have led various research
in the field of hybrid (or multiple) energy sources. Farkas et. al. in [99] presented some
possibilities for supplementing batteries with supercapacitors if the power capability of
battery technologies does not progress. Cegnar, et al. in [100] designed a HEV using a
supercapacitor as an energy source and relying on regenerative energy. For voltage
rigidity, he proposed high and low voltage supercapacitors combined with a boost DC-DC
converter. The results show the capability of the supercapacitor to capture large
regenerative currents that exceed 200A.
W. Lhomme et. al. in [101, 102] suggested supercapacitors in the energy source of a series
HEV, controlled via maximum control structure that composed of several inversion blocks
and two different management elements. The study concluded that the best compromise is
to use a battery and supercapacitor combination, though the share of energy or
specification thereof was not presented.
The topology that could offer improvement in terms of cost and power capability is a
hybridisation of a primary energy source to fulfil the requirements for vehicle range, and
an auxiliary power source to alleviate the peak power requirements of the primary energy
source, as will be discussed in this Chapter.
Supercapacitors are a strong candidate technology to play this role in vehicle applications
where braking and acceleration are prevalent. The supercapacitor has the features of high
power density, high cycle efficiency and long cycle life that complements those of electro-
chemical batteries [103-106].
Thus, by supplementing the battery with supercapacitors that assists acceleration, capture
and reuses braking energy, the energy efficiency of the vehicle power-train is improved
and makes the supercapacitor an excellent solution as complementary power delivery
device in a combination with high energy battery to form hybrid energy source system.
A combination of battery and supercapacitor could be directly connected as studied in
[107-109]. Even though this kind of connection does reduce high transient currents of the
battery, a static converter should interface the power connection between batteries and the
supercapacitor to be able to control supercapacitor energy content, maximise
supercapacitor stored energy, and to achieve higher supercapacitor convertion efficiencies
[108-110].
96
Many studies suggest the design of intermediate power converters to control the power
flow between batteries and supercapacitors. R.M. Schupbach et al. in [111] present a
comparison between a Half-bridge, Cuk and SEPIC as three DC-DC converter formats for
hybrid electric vehicles. A wide input voltage DC-DC converter using a cascaded boost
converter is proposed by Todorovic et. al. in [112] to achieve a 2:1 voltage variation at the
primary energy source interface. The converter isolates the voltage of fuel cells and
supercapacitors and allows a wide voltage change on the fuel cell (typical of fuel cell
operation). Different control schemes to manage the power flow via DC-to-DC converters
in combinations of fuel cells and supercapacitors are discussed in [113] and in [114].
M. B. Camara et. al. in [121] demonstrated two buck-boost DC-to-DC converter designs
for supercapacitor and battery power management in hybrid electric vehicles controlled by
a polynomial control strategy. One of the topologies was proposed to simplify the control
strategy, decrease the supercapacitor current and to avoid converter saturation. J. Leuchter
et. al. modelled a complete hybrid power source system including photovoltaic, battery and
supercapacitor in [116]. An energy management unit controlled a bidirectional DC-to-DC
converter to stabilise the system DC bus and achieve a high efficiency. J.W. Dixon et. al.
demonstrated a IGBT buck-boost converter and supercapacitor for an electric vehicle in
[117] to decrease the loss of the system and take advantage of regenerative braking.
In this Chapter, a combination of a battery and supercapacitor as energy sources is studied
with a view to exploit their respective energy and power attributes in an all-electric vehicle
power-train, as illustrated in Fig. 4.1. The battery represents the primary energy source
and the supercapacitor the auxiliary power source. The primary energy source is specified
and operated to fulfil the requirements for vehicle range, while the auxiliary power source
provides transient energy to alleviate the peak power requirements of the vehicle drive
cycle demands.
Fig. 4.1 Traction system layout for a all-electric vehicle.
Trans Motor EM Controller & Converter
Energy Control System Battery
Charger
Supercap. System
POWER-TRAIN DRIVE-TRAIN
TRACTION SYSTEM
97
Of the possible battery technologies proposed for battery and supercapacitor combinations,
the ZEBRA battery is chosen for the study reported in this Chapter since this is the chosen
technology of the DESERVE project [5]. The ZEBRA battery has an excellent specific
energy and a good specific power for braking and acceleration when compared with other
battery technologies. For the DESERVE project the partners have taken the basic ZEBRA
cell and redesigned it to be close to that of an earlier cell design having approximately 20%
higher energy density. However, the downside to this design change is that power density
is compromised, hence the combination of the higher energy ZEBRA with a supercapacitor
peak power buffer. The design specification for the supercapacitor power buffer is the
conclusion of this Chapter.
During the literature review, ZEBRA batteries and supercapacitors have previously been
combined by J. Dixon et. al., who demonstrated the combination in [118, 119]. The
practical tests presented showed an improvement in vehicle range and efficiency.
However, it noted that the mass of is supercapacitor bank used (132 cells of 2700 F) for a
vehicle of 1700 kg was a quite high. No consideration was made to substitute the
supercapacitor system mass with an equivalent battery mass and then reassess the range
implications. This is discussed in this Chapter thus, as with the vehicle model discussed
earlier, a case study aims to investigate that how the target vehicle performance in terms of
energy use and range could be improved with an optimum sizing of supercapacitor system.
4.3 Published supercapacitor simulation models
4.3.1 Combinations of passive elements
The impact of a supercapacitor system on the performance of any vehicle requires a
suitably detailed supercapacitor model in the overall vehicle system model. Hence, the
supercapacitor model and subsequent system design present important issues to evaluate
for varying operating conditions.
Based on the material structure of the supercapacitor and their limitations, many studies
have been published on supercapacitor behavioural models for supercapacitors that are to
be used in energy systems.
Many different circuit models have been proposed for supercapacitor, the most often
applied being the classical capacitor model [130, 131] where the supercapacitor is slowly
discharged over a few seconds, as illustrated in Fig. 4.2 showing the simplified equivalent
98
circuit. This equivalent circuit may be considered the actual capacitor behaviour in a slow
discharge application.
Fig. 4.2 Classical capacitor model.
The classical equivalent circuit consists of the capacitance, C, the equivalent series
resistance (ESR), Rs, that represents the series internal resistance that occurs during
charging and discharging, and an equivalent parallel resistance (EPR) ), Rp, to represent the
path of leakage charge which, for supercapacitors, is a long-term effect.
This model for the supercapacitor is suited for slow charge-discharge applications in the
order of a few seconds. Although the model is used widely it fails to simulate all the
dynamics of the supercapacitor cell. That is because the capacitance of a supercapacitor is
generally not constant, and is strongly dependent on terminal voltage [132, 133].
Therefore, a more effective model for supercapacitors has been modelled recently [132,
134-139]. B. Vural in [132] has provided a dynamic model for supercapacitors, as
illustrated in Fig. 4.3. The equivalent circuit model simulates the behaviour of the
supercapacitor via three resistor-capacitor (RC) branches; RC2 represents the internal
energy distribution at the end of charge and dischrage, RsC1 represents immediate cell
behaviour, and the inductance L models internal connections. The internal self discharge
behaviour is modelled by Rp.
Fig. 4.3 A dynamic model for a supercapacitor cell [132].
Rp Rs
C
99
Other published models focus on the influence of frequency on the electrode resistances
and capacitances. For example, the authors of [137] and [138] model the supercapacitor
using complex impedances the parameters of which are subsequently evaluated via
Electrochemical Impedance Spectroscopy (EIS), a common technique for characterising
electro-chemical devices, Magnitude and phase information of the various impedances are
obtained via a series of EIS tests from the capacitor under test. Model parameters are then
calculated from this data using, for example, curve-fitting routines. However, during
normal operation, voltage and temperature of the supercapacitor are likely to change
instantaneously and the dependence of the model parameters to these quantities should
therefore be considered, although they are often neglected in publications.
Fig. 4.4 Example of model layout using complex impedances [137].
An example of a model layout using complex impedances is illustrated in Fig. 4.4 showing
the supercapacitor series inductance (L), series resistance (Ri) and complex impedance
(Z(jω)). Although inclusion of the series inductance is not usually necessary for an electric
vehicle supercapacitor application (due to the transient time-base), it has to be included in
the model to avoid errors in the intermediate frequency branch of the spectrum, which
could influence the estimation of Ri. The parameter Z (jω) can be obtained as follows:
ωτωττ
ωjC
jjZ
)(coth(.)( = (4.1)
which contains only two independent parameters C which including L and Ri.
To obtain a suitable model for simulation, the frequency domain model has to be
transformed into the time domain thus providing a series expansion of RC circuits, as
shown in Fig. 4.5. The complex impedance Z(jω) can be approximated via a number “n”
of such RC circuits, which are fully described by two parameters [133, 138, 139],where, C
is the capacitance [F], in addition, R is calculated from the following equation:
][2
22Ω=
CnR n
n πτ
(4.2)
L Ri Z(jω)
100
here, n is the number of RC circuits = 1, 2, 3… n, and nτ their respective time constant(s) .
Fig. 4.5 Approximation of Z(jω) via “n” RC circuits [137].
Reported experiments show that models consisting of ten or more RC circuits show good
agreement between simulated and measured data [138]. However, it is mentioned that the
self discharge resistance is not presented and this leads to a deviation of about 5% in the
experimental and simulation results.
R. M. Nelms et al. described another approach in [139] where the supercapacitor
characteristics is modelled using a Debye polarization cell, aI schematic of which is shown
in Fig. 4.6 [139].
In the model, the circuit elements are related to the chemical reactions that occurred in the
supercapacitor. In the supercapacitor, charge is stored in the double layer formed at the
interface between a large surface area material such as activated carbon and a liquid
electrolyte. R1 is the separator resistance and depends on the concentration and
conductivity of the electrolyte used in the supercapacitor. The Helmholtz double-layer
capacitance (Cd) is influenced by: the temperature, concentration of the electrolyte and the
surface area of the electrode material. The charge transfer resistance (Rct) and adsorption
capacitance (Ca) represents charge transfer due to Faradic reactions at the surface of the
electrode material and it is affected by temperature [139]. The result of this model
suggested that total capacitance is always less than the rated capacitance.
Fig. 4.6 Circuit scheme of Debye polarization cell [139].
R1
Cd
Ca Rct
101
4.3.2 Supercapacitor models with varying capacitance
Despite being successful in many aspects, most reported supercapacitor models ignore
temperature and nonlinear capacitance, and some of them employ complex impedances.
The most recently published models have incorporated variable capacitance that depends
on the cell terminal voltage. That is, the capacitance has a voltage dependency that is
explained by the cell electric field distribution. For example, if the voltage across the
supercapacitor increases, the electric field caused by the voltage increases and attracts
more ions around the electrodes. The concentration of ions near the electrodes increases
which is manifested by increased capacitance [133, 135].
A model implementing the variable capacitance feature is described by Zubita et al in
[140]. The model uses three distinct RC time constants in three parallel branch networks,
plus a high resistance element modelling cell leakage, as illustrated in Fig. 4.7 [140].
The first or immediate branch, with the elements Ri, Ci0, and the voltage-dependent
capacitor Ci1 in (F/V), dominates the immediate behaviour of the supercapacitor in the
time range of seconds in response to a charge action. The second or delayed branch, with
parameters Rd and Cd, dominates the terminal behaviour in the range of minutes. Finally,
the third or long-term branch, with parameters Rl and Cl, determines the behaviour for
times longer than 10 min. To reflect the voltage dependence of the capacitance, the first
branch is modelled as a voltage-dependent differential capacitor. The differential capacitor
consists of a fixed capacitance Ci0 and a voltage-dependent capacitor Ci1Vci. A leakage
resistor Rp, parallel to the terminals, is added to represent the self-discharge leakage [140].
Fig. 4.7 Equivalent circuit model for supercapacitor
incorporating variable capacitance [140].
102
The equivalent circuit model of Fig. 4.7 has been simplified by J. M. Marie in [135], as
illustrated in Fig. 4.8. The model is used in power electronic applications where
supercapacitors are used to provide peak powers for only a few seconds. This means that
the second, third branches and the leakage resistance can be neglected. The capacitance in
the model is voltage dependent and Marie claims that the model shows a good agreement
when using Maxwell 2600 F cells [135].
Fig. 4.8 A simplified version of the model of Fig. 4.7 for power electronic circuit
applications [135].
When comparing between the constant capacitance and voltage variable capacitance
models Vural concluded that both models show good agreement between experimental and
simulation results when operating at the higher voltage end of the supercapacitor voltage
limits [132]. However, for low operating voltages the error between experimental and
simulation results in the constant models is increased. Moreover, for operational times
greater than 30 minutes, the agreement of the constant capacitance is less accurate by about
10% [132]. The authors in [132, 135, 140] experimentally obtained the differential
capacitance as the capacitance varies linearly with the capacitor voltage. Based on the fact
that the supercapacitor is a nonlinear capacitance and voltage dependent device, Maxwell
Technologies have developed a supercapacitor model called the reduced order model,
consisting of a series circuit having series connected resistance and inductance,
series/parallel elements modelling the equivalent series resistance and terminal parasitic
elements, a voltage dependent capacitance and a leakage element, as illustrated in Fig. 4.9.
The model and laboratory cell characterisation for different cell sizes is presented in [135]
where the bulk capacitance is related to terminal voltage by the linearised equation and the
parameter values are as in [135]:
cvc VkCVC += 0)( (4.3)
The model presented in [135] has been implemented in the Simplorer software, but the
author only presented the simple varying capacitance case of Eqn. (4.3). Further, although
the author presented a thermal model for an individual cell, multiple cell systems
modelling was not reported.
103
Fig. 4.9 Maxwell model presented in [135].
4.4 The proposed supercapacitor model
4.4.1 Matlab/Simulink model
In this study the Maxwell supercapacitor model of Fig. 4.9 is built in the Matlab/Simulink
environment using Matlab/Simpower toolbox components, but using a variable non-linear
capacitance function determined from test and implementing a thermal model as part of the
full cell model. The developed Matlab/Simulink model is illustrated in Fig. 4.10, showing
the main passive circuit components (as for Fig. 4.9), the bi-polar input/output current
demand, a variable non-linear capacitance function block, cell power loss calculation block
and cell/module thermal model. The non-linear capacitance function is implemented via a
look-up table of capacitance versus terminal voltage data embedded within the block, as
illustrated in Fig. 4.11 showing the block internal details as presented by Simulink.
104
Fig. 4.10 Maxwell model in Simpower Matlab.
Fig. 4.11 Details of the non-linear capacitance function block showing the look-up table.
Non-linear capacitance
function
Demand current
Loss calculation for input to
thermal model
Thermal model
105
Note, there are several ways to connect the Simulink part of the model with the electrical
circuits modelled in Simpower, such as a controlled current source and a controlled voltage
source. The controlled voltage source has been chosen in the model because it can be
easily implemented using the Simpower tool box in Matlab and it provides good results in
terms of simulation stability.
The controlled voltage source model the non-linear capacitance according to the equation:
dtC
titv ∫= )()(
(4.4)
Thus, current should be measured to calculate the voltage (signal) that controls the voltage
source. The variable capacitance look-up table uses the voltage to determine the
corresponding value of capacitance from the voltage versus capacitance data, as shown in
Fig. 4.12. Integration is then performed to calculate the voltage connected to the
controlled voltage source. The look-up table data is taken from a series of tests undertaken
on a supercapacitor system comprising of 3x 48 volt, 165 F supecapacitors connected in
series, as will be discussed later.
0 0.5 1 1.5 2 2.5 31500
2000
2500
3000
3500
Cap
acita
nce
(F
)
Terminal Voltage (V)
Fig. 4.12 Supercapacitor cell non-linear capacitance versus voltage
function determined from test.
106
4.4.2 Parameter temperature considerations
To consider the use of a supercapacitor in any vehicular application, the thermal behaviour
of the supercapacitor must be understood, particularly with regard to cyclic loading and the
varying ambient temperatures associated with vehicle applications as already discussed. It
is reported in [142] that the Maxwell supercapacitor equivalent circuit parameters are
essentially unaffected by temperature, being stable in terms of capacitance change over the
specified operating range (which is between -40º to +65º C), as illustrated in Fig. 4.13(a)
[142]. This is attributed to the fact that the charge storage is not a chemical reaction, and is
one of the advantages of supercapacitors in low temperature applications compared to
electro-chemical batteries. Note however that the upper operating temperature of +65º C
could still be prohibitively low in some climates and would necessitate forced or liquid
cooling if the ambient is raised closed to this level. The resistance is slightly affected over
the operation range due to ion mobility within the electrolyte, although being relatively
stable over the 0º to +70º C range.
Other authors have considered the effected of temperature changes on supercapacitor
dynamics, as discussed in [132-134, 143], where the authors show that there is a slight
change in supercapacitor capacitance over the operated temperature range, as illustrated in
Fig 4.13(b). It is noticeable that the capacitance is almost stable at temperatures above
zero, while the resistance is affected at low temperature, but with a very shallow change at
temperatures above zero, as shown in Fig 4.13(b). Although the supercapacitor dynamics
are affected at low temperature, in this study the operated temperature range of the
supercapacitor system is assumed to be around a min. ambient temperature of 5oC. Hence,
the resistance and capacitance changes are assumed negligible.
107
(a) Capacitance and resistance variation with temperature as reported in [142].
(b) Capacitance and resistance variation with temperature as reported in [134] and [143].
Fig. 4.13 Reported supercapacitor parameter variation with temperature.
4.4.3 Thermal model
During supercapacitor charging and discharging operation, power loss is dissipated in the
supercapacitor cell resulting in an increase in the cell temperature. The power loss
dissipation leads to heat absorption into the cell thermal capacity and conduction to the cell
outer surface via its thermal resistance. In the supercapacitor cell block model illustrated
in Fig. 4.10, the power loss in each resistive element is summed within a block and this
then input to the thermal equivalent circuit of each cell, as shown in Fig. 4.14. Here, the
total cell power loss represents the thermal the heat source and values for the cell thermal
resistance to ambient, Rth, and cell thermal capacitance, Cth, are taken from Maxwell
supercapacitor cell of 3000F data sheets, as given in Table 4.1 [135].
Temperature (ºC)
108
Rth 3.2 ºC/W passive
Cth 588 J/ºC
Operating temperature -40ºC to +65ºC
Storage temperature -40ºC to +70ºC
Table 4.1 Thermal parameters for Maxwell 3000F cell supercapacitor [135].
Fig. 4.14 Supercapacitor thermal equivalent circuit model.
Tº
25º
109
4.4.4. Matlab/Simulink supercapacitor model validation
The supercapacitor model was initially validated by comparing published constant current
discharge profiles for the Maxwell 3000 F cells obtained from [141] with data generated by
the model simulated with a constant current discharge demand, as illustrated in Fig. 4.15,
showing a good agreement between published and simulated data. The difference between
the published and simulated data is mainly at the lower voltage level which is acceptable
since the supercapacitor will not typically be discharged below 25% of the maximum DC
voltage level due to power electronic and control considerations.
0.5
1
1.5
2
2.5
3
0 30 60 90 120 150 180 210 240Time (s)
Vol
tage
(V
)
Maxwell
sim
Fig. 4.15 Comparison between published and simulation model results.
As part of the DESERVE project [5], 3x 48V Maxwell supercapacitor modules, each
comprising of 18, series connected, 3000F cells (total 165F per module), were purchased
for subsequent installation on the project vehicle. The nominal supercapacitor module
voltage of 48 arises from an upper operational limit of 2.6 to 2.7 V per cell. Tests carried
out on the Maxwell modules were used to develop the Matlab/Simulink model capacitance
versus terminal voltage characteristic of Fig. 4.12, validate the model energy calculations
and thermal model.
The 3x 48V modules, SC1, SC2 and SC3, were series connected and then connected to the
output of a Ward-Leonard (W-L) motor-generator set (DC) via DC contactors, as
illustrated in Fig. 4.16 showing the power circuit schematic of the supercapacitor module
test facility and their respective voltage, current and temperature measurements. A picture
150A
10A
50A 100A
200A
110
gallery of the supercapacitor module test facility main components is illustrated in Fig.
4.17, showing the PC for Labview control of the W-L set, voltage, current and temperature
data acquisition (a), the W-L set brushed DC and induction machines for implementation
of a controlled DC supply (b), the 3x 48 Volt, 165 F Maxwell supercapacitor units (c) and
measurement PCBs for the interface of voltages, currents and temperatures to the Labview
data acquisition system (d).
Control of the field winding of the Ward-Leonard DC motor-generator results in a
controlled DC-voltage at the terminals of the 3x 48V supercapacitor module bank. Typical
current peaks are in the order of 200A, with a peak power capability from the W-L set of
25kW. To test the supercapacitor capacitance-voltage characteristic, the supercapacitor
bank was charged to some initial voltage level. A Labview based controller was then used
to control alternate charge-discharge currents to the supercapacitor bank from the W-L set
with two primary goals:
• to maintain neutral charge, or the DC-link charge-discharge voltage within a fixed
envelope, during a cycle, and
• to control repetitive cycles and hence an essentially constant internal loss in order to
assess the thermal impact on the combined supercapacitor bank.
A series of tests were carried out based on varying the initial voltage level and the
Matlab/Simulink model capacitance versus terminal voltage characteristic of Fig. 4.12
determined. An example is illustrated in Fig. 4.18 showing a Maxwell 165F module DC
voltage variation due to alternate charge-discharge currents from the W-L set. The test is
executed for approximately 2500 seconds whereon the supercapacitors are discharged and
the temperatures monitored back to ambient (total test time of around 10,000 seconds). It
should be noted that the module voltage transitions are maintained within a 7.5 to 27.5 V
envelope on a mean of 17.5V. Fig. 4.19 illustrates a zoom in on this test showing
approximately 3 cycles of data.
Table 4.2 presents example calculations for the case where the Matlab/Simulink
supercapacitor model is simulated with the same input charge-discharge current profile as
illustrated in Fig. 4.19. By taking the average values of current and voltage, the table
results show good correlation between calculated and measured supercapacitor input-
output energies.
111
Fig. 4.16 Power circuit schematic of supercapacitor module test facility.
Fig. 4.17 Picture gallery of supercapacitor module test facility.
ISCT IDC
Fieldcontrol
SC1 T1
En1
T2
En2
T3
En3
DC
VSC1
VSC2
VSC3
VSCT VDC
SC2
SC3
ControlcontactorM1
ISCT IDC
Fieldcontrol
SC1 T1
En1
T2
En2
T3
En3
DC
VSC1VSC1
VSC2VSC2
VSC3VSC3
VSCT VDC
SC2
SC3
ControlcontactorM1
(a) PC for Labview control and data acquisition
(b) Ward-Leonard for controlled DC supply
(d) Measurement PCBs
(c) 3x 48 Volt, 165 F Maxwell supercapacitor units
112
Fig. 4.18 Maxwell 165F module DC voltage variation due to alternate
charge-discharge currents from the W-L set.
Fig. 4.19 Maxwell 165F module DC voltage and current variation
due to alternate charge-discharge currents from the W-L set.
SC Energy Loss
(kWh)
SC Input Energy (kWh)
SC Ouput Energy (kWh)
SC Energy efficiency
(%)
Simulation model 0.45 17.34 16.89 97.40
Measured 0.41 17.00 16.59 97.60
Note: Energy efficiency is over cycle duration
Table 4.2 Example comparison of simulated and measured energies for the charge-
0
5
10
15
20
25
30
25 50 75 100 125 150
Time (s)
Mod
ule
volta
ge
(V)
.
-200
-150
-100
-50
0
50
100
150
Mo
dule
cur
rent
(A
) .
Voltage Current
0
5
10
15
20
25
30
0 250 500 750 1000 1250 1500 1750 2000 2250 2500
Time (s)
Mo
du
le v
olta
ge
(V)
.
113
discharge current profile illustrated in Fig. 4.19.
The repetitive test cycles, as illustrated in Fig. 4.18, result in an essentially constant
internal loss within the supercapacitor modules. This constant loss is used to assess the
module thermal performance and also confirm the suitability of the thermal simulation
model discussed in Section 4.4.3. Fig. 4.20 illustrates a comparison of the simulated
supercapacitor module temperature and measured temperatures taken around the Maxwell
module, again showing good agreement. The laboratory ambient is also included
highlighting a near fixed ambient during test. The results of Fig. 4.20 illustrate that the
predicted temperature lies between the minimum and maximum measured temperatures,
being closer to the maximum, which would be expected due to the transducer placements.
Fig. 4.20 Comparison of simulated and measured temperature of Maxwell module during
repetitive cycle regime as Fig. 4.18.
20
25
30
35
40
45
0 500 1000 1500 2000 2500 3000
Time (s)
Te
mpe
ratu
re (
degr
ees
C)
.
Maximum recorded temperature
Minimum recorded temperature
Ambient temperature
Model predicted temperature
114
4.5 Vehicle on-board energy management
4.5.1 Principle of energy storage The energy storage capacity for a supercapacitor can be described by equation 4.5 [125]:
E = 0.5CV2 [4.5]
where E is the stored energy in Joules [J], V is voltage of the supercapacitor and C is
capacitance [F]. The distinguishing feature of supercapacitors is their particularly high
capacitance. Another measure of supercapacitor performance is the ability to store and
release the energy rapidly. This is the power density, P, of a supercapacitor and is given
by:
R
VP
4
2
= [4.6]
where, R is the internal resistance of the supercapacitor (ESR) [126].
4.5.2 Recovered energy
Having multiple energy source systems in EV highlights the importance of coordinating
and arbitrating power sharing between the system components. One of the benefits of
combining battery and supercapacitor in the electric vehicle of a rail based application is
the ability to save more 30% of total energy and recover regenerative energy [144].
The proposed control strategy is based on the diversion all recovered energy to the
supercapacitor, to guarantee that the supercapacitor should be fully charged when the
vehicle is at standstill because the possible next step is only to accelerate. However, the
amount of regerative energy gets back to the system when the vehicle at maximum speed,
because the possible next step is to decelerate.
The propulsion system power-train infrastructure from the energy source to the traction
wheels must be bidirectional and the energy source has to be receptive to regenerative
energy. The recovered energy will be either transferred to the supercapacitor or to the
battery. The most efficient way is to transfer it to the supercapacitor to store the energy for
the next acceleration period.
To obtain whole regenerative energy from supercapacitor, the energy rating should be
higher. In order to determine the sufficient energy in the supercapacitor, all power losses
115
should be taken into account. During regenerative braking, the kinetic energy of the
vehicle should be fully converted and captured by the energy system through DC link.
Practically, only 30% to 50% of this energy is recoverable due to electrical and mechanical
losses when transferring power from the supercapacitor to the wheels [24], as illustrated in
Fig. 4.21.
wheelsfrom capturedenergy
at wheels avaliableenergy . =regenη
[4.7]
.... .. convtractransregen ηηηη = [4.8]
The amount of regenerative energy is depends on many factors such as motor, deceleration
rate and receptiveness of the energy storage system. In a rapid deceleration event, the
magnitude of power would be high in short period of time, which means a motor of a high
power rating is needed to capture that power. For a maximum energy capture, the change
in kinetic energy should be equal storage energy.
Fig.4.21 Electrical losses in the power-train.
4.5.3 Power-train losses
The DC-to-DC converter is a device that transfers DC power of one voltage level to
another level. Practically there are some losses during the process. The converter gain is
the ratio of output to input voltage as shown in Fig. 4.22, it is clear that the gain is linear in
only the blue bit area, so working in this area, the converter could be modelled as a gain
116
[21]. The loss could be calculated by setting the converter efficiency at a constant, the
losses, hence, could be determined.
Fig. 4.22 The converter gain for DC-to-DC converter [ 21].
The DC-to-DC converter losses is basically based on power transfer; supercapacitor power
(PSC) could be found based on the following:
Boost case: (power transfer from supercapacitor to DC link)
LbatSC PPP += . [4.9]
Buck case: (power transfer from DC link to Supercapacitor)
LbatSC PPP −= . [4.10]
To find out a supercapacitor current
SC
SCSC V
PI =
[4.11]
The losses are classified as:
• IGBT power losses which including Power silicon losses which calculated from the
turn, Conduction losses, Turn-off losses, Diode reverse recovery losses, and Diode
losses,
• Inductor losses, and
• Capacitor losses.
117
The drive-train losses are that occur in the subsystems such the traction motor and the
transmission losses. The traction motor losses are the losses that occur during motoring and
generator. The traction motor is a brushless permanent magnet machine with an integrated
gear reduction and differential drive to the vehicle back-axle. The machine is controlled
via a three phase voltage source converter, the DC supply to which is provided by the
traction battery. In the vehicle model as mentioned earlier, the traction motor loss is
calculated from efficiency map.
4.5.4 Control strategy
Different control strategies have been proposed to manage energy in hybrid systems. The
control aims to make main energy source, which means battery, to work at a constant or
low fluctuation current much lower than the demand, and to make the supercapacitor,
which has ability to capture and release energy very rapidly, provides most demand
transient current. In energy point view, the role of the battery is to provide the mean energy
requirements of the traction drive system, while the supercapacitor current exhibits the
high frequency transients of the traction machine drive.
Based on the proposed control strategy, the supercapacitor should be fully charged when
the vehicle at standstill state because the possible next step is only to accelerate. However,
the amount of regerative energy gets back to the system when the vehicle at maximum
speed, because the possible next step is to decelerate.
4.5.5 Phase compensating regulator design To facilitate a design of a suitable control strategy and an optimal management of energy
and power transfer between the components of the vehicle that shown in Fig. 4.23 (a), a
controller objective should be classified. Here the objectives of the applied controller are:
• Track a demanded velocity profile for the vehicle,
• Maintain the DC link voltage within given bounds,
• Minimise battery current fluctuations.
DC link voltage control uses a voltage reference as energy management control parameter,
when the DC link voltage is subjected to load current as disturbance, the principle of this
method is to regulate DC link voltage within a tolerance band around a set point reference
118
voltage so the DC link is controlled the dips and rises during acceleration and braking
respectively.
The control loop, presented in Fig. 4.23 (b), used to regulate DC link voltage for the
system. The controller is designed by driving analytical expressions of the system, and
applying classical control design techniques.
The controller has been applied to the system model using a DC-link voltage regulator
configuration to maintain the DC-link voltage within given bounds and fully
supercapacitor utilisation and reducing the transient demand from the battery.
In order to design the controller, a mathematical description of the plant must be
developed.
The current drawn by the traction drive (Iload) will act as a disturbance on the VDC
regulator. In the event of a load disturbance, causing a supercapacitor discharge, the
control loop will act to force VDC to a desired value by application of battery current to
recharge supercapacitor
Load
DC DC
Load
DC DC
ZEBRA System
I bat.
I SC
I Load
SC Bank
(a) System electrical schematic.
Regulator DC/DC
Converter SC
Bank
IL
_
+
Vsc
+ _
VDC|
(b) Control system schematic.
Fig.4.23 Regulated DC link voltage control scheme.
The supercapacitor voltage is modelled using the Laplace differential operator s:
( ) ( )sc
scscscDC sC
RsCsIsV
)1( +−=
[4.12]
119
The capacitor will act as an integrator for low frequency components of current due to the
pole, located at the origin, therefore, a finite DC component of current will result in a
voltage ramp across the capacitor. The zero is located at 1.1364 rads-1, as shown in Figs.
4.24 and 4.25, for frequencies above that the gain will asymptotically become a constant
value of Rsc. The sign of the current is negative as the current polarity is defined as positive
for discharging.
For the supercapacitor voltage control loop, the supercapacitor current may be determined
from Fig 4.23 (a) where
Batloadsc III −= [4.13]
-1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2-0.06
-0.04
-0.02
0
0.02
0.04
0.06Open loop Plant
Real Axis
Fig. 4.24 Poles and zeros of the plant.
The voltage across the supercapacitor may be expressed as equation
loadsc
scscBat
sc
scscDC I
sC
RsCI
sC
RsCV
)1()1( +−
+=
[4.14]
The first term describes the relationship between the VDC state and the battery current,
where the second term is associated with a disturbance Iload, therefore the supercapacitor
plant may be expressed in the transfer function as
s
sRC
RI
V scscsc
Bat
DC
+
=
1
[4.15]
Real axis
Imag
inar
y ax
is
120
The plant gain is a constant for high frequencies, as shown in Fig. 4.25, which will lead to
undesired high frequency noise. The response of the open loop plant is shown in Fig. 4.26.
-40
-30
-20
-10
0
10
20
10-2
10-1
100
101
102
-90
-45
0
Open loop Plant
Fig. 4.25 Open-loop Bode plots of the plant.
0 500 1000 15000
5
10
15
20
25
30
Step Response
Am
plit
ude
Time (s)
Fig. 4.26 Open-loop plant response.
To overcome this phase compensating regulator is introduced of in the form of equation:
Mag
nitu
de
(dB
)
Frequency (rad/s)
Ph
ase
(deg
)
121
DCerrbat V
as
bI
+= [4.16]
It is normally possible to adjust the system’s response sufficiently to achieve the required
performance [145]. The open-loop transfer function is given as:
)(
1
ass
sRC
RbV
V scscsc
DCerr
DC
+
+
= [4.17]
The location of supercapacitor zero is 1.1364 rads-1, so to provide higher frequency
attenuation, the location of the controller pole will set at 1.5 rads-1, for this value of a=1.5.
To select DC-link voltage controller bandwidth, the dominant closed loop time constant is
equal to the longest period of acceleration in the driving cycle; hence the longest
acceleration period of the driving cycle used is 30 seconds, then the close loop bandwidth
will be set to 0.03333 rads-1.
To set the bandwidth of the supercapacitor voltage regulator to 0.03333rads-1, the gain is
chosen as one i.e. 1=DCerr
DC
V
V, Thus:
( 1.1364 0.03333)1 0.016
0.03333(1.5 0.03333)
2.7495
jb
j j
b
− +=+
=
[4.18]
The poles and zeros of the controller is shown in Fig. 4.27, the response of the system is
shown in Fig. 4.28. The Bode plot of the close loop system is shown in Fig. 4.29.
-5 -4 -3 -2 -1 0 1-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Real Axis
Real axis
Imag
inar
y ax
is
Fig. 4.27 Poles and zeros of the system.
122
0 20 40 60 80 100 120 140 160 1800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Closed loop Plant + Controllers
Am
plit
ud
e
Time (s)
Fig. 4.28 Close-loop system response.
-50
-40
-30
-20
-10
0
Mag
nitu
de (dB
)
10-3
10-2
10-1
100
101
-90
-45
0
Pha
se (de
g)
Closed loop Plant + Controllers
Ph
ase
(d
eg
)
Frequency (rad/s)
Ma
gnitu
de
(dB
)
4.29 Close-loop Bode plot of the system.
The demanded DC link voltage may be controlled by taking desired DC voltage as voltage
reference, the desired signal is to control the level of energy in supercapacitor to match the
kinetic energy of the vehicle:
sc
UscDC C
mvVCV
)( 22| −=
[4.19]
where, VDC| is the regulated DC link voltage, CSC is the capacitance of the supercapacitor;
VU is the terminal voltage, and mv2 is the kinetic energy.
123
According to the concept of the control, the regulated DC link voltage should be equal to
the upper bound when the vehicle is standstill for an expected acceleration.
In addition, the demanded DC link voltage should be equal to the lower bound When the
vehicle at full speed, so the regenerative should be captured in the supercapacitor from the
next expected step which is deceleration or breaking.
4.6 Sizing of supercapacitor power buffer To use supercapacitor as energy storage system and to avoid over sizing supercapacitor
and then additional costs, and mass of the vehicle, it is important to calculate the proper
size supercapacitor to capture most of available braking energy for any velocity profile
[146].
4.6.1 Vehicle energy and power considerations Design and sizing an supercapacitor to meet the propulsion demands for a given velocity
profile is obtained from the regenerative energy, as well as considering mass, volume, cost
and high efficiency. For the DESERVE project [5] a vehicle of 3.5 tonne is chosen as the
case study, the vehicle and power-train parameters shown in detailed in Table 4.3. The
main energy source is the DESERVE ZEBRA battery; its parameters are shown in Table
4.4, combined with a supercapacitor power buffer, the design of which is discussed here.
4.6.2 Supercapacitor size
Initial sizing calculations for the supercapacitor system were based on the simulation
results of a case study vehicle over the DESERVE_ECE15 driving cycle shown in Fig.
4.30. It is chosen to be an example of the velocity profile which being the supercapacitor
sized accordingly to recapture all of the available regenerative braking energy.
The maximum and minimum supercapacitor voltage should be calculated from the
convertor stage and battery voltage, as mentioned in [105], in Fig. 4.22 if the conversion
factor of dc converter is 4 or less; input to output relationship is linear [21]. In this case
study the battery voltage is 320V, to obtain the minimum voltage of the supercapacitor of
80 V, (i.e. 320/4).
For practical application it is acceptable discharging on 50% voltage drop from its nominal
value, therefore for supercapacitor with a nominal voltage decrease on half of its value is
acceptable [105], 80 V is considered to be the half of the maximum value, which means
124
that the supercapacitor voltage VSC =160V. The upper and lower boundaries are 80, 160
respectively to grant the DC converter is linear.
Model parameter Description Value Units
n Gear and deferential ratio 11.95 -
Jm Machine inertia 5.70x10-4 kg/m2
Jw Wheel inertia 0.164 kg/m2
η Machine efficiency 1.0 p.u.
df Distribution factor 1 -
rw Tyre radius 0.364 m
m Mass during test 3500 kg
Kr Co-efficient of rolling resistance 0.0267 -
g Gravitational constant 9.80665 m/s2
ρ Density of the air 1.23 kg/m3
Cd Co- efficient of drag 0.31 -
Af Frontal area 1.75 m2
Table 4.3 3.5 Tonne vehicle and power-train parameters for simulation model.
Parameter Value Units
Capacity 76 Ah
Rated energy 21.2 kWh
Open circuit voltage 279 V
Total weight 195 kg
Max. discharge current 224 A
Cell type DESERVE cell -
No. of cells in series 120 cells
No. of strings in parallel 2 string
Table 4.4 DESERVE ZEBRA battery parameters.
125
0
10
20
30
40
50
60
0 50 100 150 200
Time (s)
Speed (km
/h)
Fig.4.30 DESERVE_ECE15 driving cycle.
The kinetic energy of the vehicle is given by:
2
21
mvWk = [4.20]
The operation stored energy from the supercapacitor system is determined by its upper and
lower limits:
)(21 22
LUscsc VVCW −= [4.21]
If a 2:1 operational voltage variation is assumed, 75% of the supercapacitor stored energy
is available for the power-train, i.e.
)(83
43
21 22
UscUscsc VCVCW
=
= [4.22]
According to the control strategy, the total kinetic energy is recoverable, so the minimum
capacitance could be calculated from:
sck WW = [4.23]
Then:
)(3
82
U
Ksc
V
WC =
[4.24]
In case of the DESERVE_ECE15, the peak linear velocity is 14m/s, and the mass of
Edison van is 3500kg, the kinetic energy is equal to:
126
MJW 343.0= [4.25]
So, the total capacitance ( )scC required is 35.7F.
In order to evaluate the performance of the vehicle model over DESERVE_ECE15 driving
cycle, the losses of all components in the power train are calculated and simulated to be
50%, then:
( )MJWsc 515.0()5.0343.0343.0 =×+= [4.26]
Then the ideal capacitance is calculated to be:
CSC=53.6F
The Maxwell supercapacitor systems were the only real technology that was near market in
terms of the TSB remits. All other technologies were being offered at individual cell level
and with very little to no technical support or applications track-record. Hence, Maxwell
supercapacitors were the chosen technology for the vehicle validation platform. Maxwell
supercapacitor cell (3000F) is chosen in this case study, in order to assemble the total
capacitance.
totalP
scell C
N
NC =
[4.27]
Ns is the number of cells in series, and NP is the number of cells in parallel. The number of
cells in parallel is determined after the first iteration of the calculation, if the first iteration
indicates that there is a inadequate capacitance for the requirement, the capacitance can be
changed either by putting more cell in parallel or by using larger cells [111]. Regarding to
that NP is equal to 1 in this case, thus Ns= 56 cells in series.
From various systems available from Maxwell, the 125V and 48V units were the most
likely contenders since these components are ready stock items that have some industry
track record. BMOD0165-48.6V [141] is chosen to provide the total capacitance, three of
(BMOD0165-48.6V),which consists of 18 cells connected in series, are connected in series
to provide a terminal voltage of 145.8V and a capacitance of 56F, as illustrated in Fig.
4.31.
Recalculating the available energy in the selected supercapacitor bank between the voltage
boundaries (145.8-80V):
)(5.0 22
. LUsc VVCW −= [4.28]
By considering the 50% losses, the available energy should be equal or more than the
needed energy:
Ksc WMJW ≥= 6.0 [4.29]
127
Individual unit voltage measurements (red)
Total series current measurement (blue)
Individual unit voltage measurements (red)
Total series current measurement (blue)
Fig.4.31 Connection scheme of three of Maxwell 165 F supercapacitor
modules into 146V, 56 F bank.
The case study is illustrated in Fig. 4.32.
Fig.4.32 The case study of the combination.
4.6.3 Full model simulation results
Some results of terminal voltages of both energy sources are shown in Fig. 4.33, while Fig.
4.34 shows the demand current, ZEBRA battery and supercapacitor currents.
The terminal voltage variation is very important on the battery life as well as inverter
design. The variation of the voltage defined as the dip voltage that occurs regarding to
discharging current.
128
800 850 900 95050
100
150
200
250
300
Times (S)
Vo
ltag
e (V
)
Battery VoltageSC Voltage
Fig.4.33 Terminal voltages of both energy sources.
800 850 900 950-200
-150
-100
-50
0
50
100
150
200
Times (s)
Cu
rren
ts (
A)
SC currentDemand currentBattery current
Fig. 4.34 Currents of demand, battery, and supercapacitor.
To illustrate the impact of tiding up that variation range of the vehicle of 3500kg van over
DESERVE_ECE15 driving cycle, the model evaluated in two operation modes; pure
battery operation and battery with supercapacitor operation mode. Additionally, to
illustrate the comparison and to show the impact clearly, the third case considered the
energy system mass and called extra battery. The case presents a pure battery which its
mass is equivalent to the proposed system; this can be done by adding the weight of the
129
supercapacitor system (supercapacitor bank with DC-to-DC converter) to the original
battery mass.
The mass of the supercapacitor system is calculated as three modules of Maxwell
(BMOD0165-48.6V), each weight 14.2kg [141]. The average mass of DC-to-DC
converter is about 25 kg, and then the total mass of the system is 67.6kg. This number
represents 0.17 of the total mass which are two ZEBRA batteries each 195kg (as in Table
4.4].
Pure Battery Extra Battery Battery with SC
DV Range km SOC Range km SOC Range km SOC
1.4 85.8 0.1 93.7 0.1 93.2 0.1
1.2 73.3 0.24 85.8 0.26 93.2 0.1
1 57.4 0.4 66.5 0.4 71.7 0.3
0.9 26.5 0.74 30.9 0.74 55.5 0.4
0.8 1.2 0.99 1.5 0.99 33.1 0.7 Table 4.5 Simulated range versus the voltage variation (DV).
The result shown clearly as in Fig. 4.35 and Table 4.5 that in the case of battery and
supercapacitor combination, the range is improved as well as the energy in the battery is
used efficiently. It is clear that the combination of ZEBRA and supercapacitor can
improved the terminal voltage fluctuation by 1 volt/cell in the battery. In addition, the
energy loss in the battery system is decreased from 10.5 and 8.5 kWh in extra battery and
original battery respectively to 5.3 kWh in a combination case, hence the energy loss of the
power-train is decreased, and hence the efficiency of the system is improved.
0
10
20
30
40
50
60
70
80
90
100
0.8 0.9 1 1.1 1.2 1.3 1.4
Voltage variation (DV) per cell (V)
Ra
nge
(km
)
Battery Only
Extra Battery
Battery with SC
Fig. 4.35 The range of the vehicle as the terminal voltage variation is tightened.
130
Reducing the voltage variation could be done by adjusting the battery current using a
current limiter on the battery current, as battery current is decreasing, more relief on
battery and more range improvement as shown clearly in Fig.4.36 and Table 4.6 where the
battery current is decreasing from 75A to 25A. In addition, the power loss in the battery
system is reduced from 5.1 at 75A to 3.4 kWh at 25A. The voltage variation in the cases
of 75, 50, and 25A are shown in Fig. 4.37-4.39.
Ibat.=75A
Ibat.=50A
Ibat.=25A
V∆
Range SOC Range SOC Range SOC
1.4 93.4 0.1 97.8 0.1 111.2 0.1
1.2 93.4 0.1 97.8 0.1 111.2 0.1
1 73.7 0.3 80.9 0.3 111.2 0.1
0.8 40.2 0.6 60.4 0.5 84.3 0.3
Table 4.6 The range of the three currents with battery SOC.
0
20
40
60
80
100
120
0.8 0.9 1 1.1 1.2 1.3 1.4
Voltage variation/cell (V)
Ran
ge(k
m)
Ibat=75AIbat=50AIbat=25A
)
Fig. 4.36 The impact on the range when battery current limit is decreased.
131
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
50
100
150
200
250
300
350
Time (s)
Vol
tage
(V)
Supercapacitor Voltage
Battery Voltage at 75A
(a) Variation in terminal voltages.
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 20000
50
100
150
200
250
300
350
Time (s)
Vol
tage
(V
)
Battery Voltage at 75A
Supercapacitor Voltage
(b) Timed-zoom for battery and supercapacitor voltage.
Fig. 4.37 Voltages when battery current limit is 75A.
132
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 104
0
50
100
150
200
250
300
350
Time (s)
Vol
tage
(V)
Battery Voltage at 50A
Supercapacitor Voltage
(a) Variation in terminal voltages.
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 20000
50
100
150
200
250
300
350
Time (s)
Battery Voltage at 50A
Supercapacitor Voltage
(b) Timed-zoom for battery and supercapacitor voltage.
Fig. 4.38 Voltages when battery current limit is 50A.
133
0 0.5 1 1.5 2 2.5
x 104
0
50
100
150
200
250
300
350
Time (s)
Vol
tage
(V)
Battery Voltage at 25A
Supercapacitor Voltage
(a) Variation in terminal voltages.
1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 20000
50
100
150
200
250
300
350
Time (s)
Vol
tage
(V
)
Battery Voltage at 25A
Supercapacitor Voltage
(b) Timed-zoom for battery and supercapacitor voltage.
Fig. 4.39 Voltages when battery current limit is 25A.
134
4.7 Summary
The supercapacitor is simulated as nonlinear voltage dependent capacitance, the model is
validated. The impact of temperature on supercapacitor bank is simulated and tested in the
laboratory. The control strategy was based on that the recovered energy, which transferred
to the supercapacitor proposed and designed.
Based on the vehicle model, the supercapacitor system for 3500 kg van over the
DESERVE ECE15 driving cycle is sized. As a result of the combination of ZEBRA
battery and supercapacitor, the battery mass is downsized by a approximately 17%.
At a fixed mass of battery, the performance of the vehicle is improved by:
• Increasing battery life by decreasing battery current fluctuation, which means
decreasing discharge/ charge current that drawn from the battery.
• The range is extended by about 24%.
• Better energy utilisation.
• Reducing the voltage variation on the battery voltage that makes the inverter
design simpler and less loss.
135
CHAPTER 5
DOWNSIZIED AUXILIARY POWER UNIT IN A SERIES HYBRID POWER-TRAIN CONFIGRATION
5.1 Introduction The electric vehicle model is used to investigate and analysis a hybridisation of main
energy sources, i.e. ZEBRA battery and an auxiliary power unit, for a series hybrid electric
vehicle. A dynamic model of ICE/PM generator is developed and used as auxiliary power
unit in the model to configure a series hybrid power-train.
The objective of this Chapter is to design an optimized and downsized ICE/PM generator
in a series hybrid power-train configuration. Vehicle performance, driving range, and
battery utilization over many driving cycles for different case studies are presented. In
series hybrid electric vehicle, the ICE is completely decoupled from the drive train and it
used only to generate electrical power to improve fuel economy, reduce harmful emissions
compared to conventional ICE, and also to extend the limited battery range. This kind of
configuration is attractive for large vehicles that perform large amounts of stop-and-go
driving, such as buses, taxis, and delivery trucks. This kind of vehicle is highly inefficient
and produces high levels of emissions because they have a large engine. A hybrid electric
vehicle, as mentioned in Chapter 1, is a contender vehicle power-train since they alleviate
some drawbacks experienced with all electric vehicles in terms of range while providing an
intermediate power-train in the move from conventional to an all electric rational.
Recently, power assist hybrid electric vehicles, such as parallel and series parallel gasoline
ICE hybrid vehicles have been promoted by automotive manufactures and suppliers as an
effective way to achieve driving range equal to conventional ICE vehicles with less fuel
consumptions and less emissions.
136
5.2 Hybridization ratio and ICE/HPM generator rating One of the important design criteria of hybrid combustion engine vehicles is the relative
size of the battery and engine, as illustrated in Fig. 5.1.The Hybridization Ratio (HR) is to
measure how strongly the energy sources in the power-train is hybridized [147, 23]. In
other words, HR can be characterized in terms of battery and ICE/HPM generator ratings
(capability) or in terms of the percentage share of battery and ICE/HPM generator power,
which depends on the driving cycle peak and average power requirements.
Fig. 5.1 Hybridisation of battery and ICE strategy in HEV.
Far from conventional vehicle, it is essential in a hybrid vehicle to operate each power-
train component at optimal efficiency to improve the overall efficiency of the vehicle.
Generally, the combination of ICE and battery system aims to optimise ICE engine
performance in best efficiency operating region, moreover, as a result of that fuel
consumption and gas emission are reduced.
ICE provides constant power is one of scenarios to optimise operating region of ICE in the
hybrid vehicles. The ICE in this case can be smaller and more efficient, because it provides
only a constant power, while the dynamic power is provided from another source and it
provides only a constant power, as shown in Fig. 5.2.
Fig. 5.2 Method to split power between two energy sources [23]
Battery size
ICE size
Conventional ICE vehicle
Pure electric vehicle
% Hybridisation ratio
-20
-10
0
10
20
30
40
50
60
0 5 10 15 20
Time (s)
Pow
er (
kW)
= +
0
0.5
1
1.5
2
2.5
3
3.5
0 5 10 15 20
Time (s)
Pow
er (
kW)
-20
-10
0
10
20
30
40
50
60
0 5 10 15 20
Time (s)
Pow
er (
kW)
137
By applying that concept in series hybrid vehicle, a small engine is used to rotate a
generator at a constant speed and power output level. This set is combined with battery
system. When vehicle power requirements increases (acceleration), additional power is
drawn from an on board energy storage and when power requirements decreases, the
energy storage system is recharged. By this kind of structure, vehicle can recapture
regenerative braking, and increase engine efficiency.
To size the battery and ICE/HPM generator in the proposed series hybrid power-train
configuration, some driving cycles should be considered. For example, by using the
simulation results of any driving cycle, the energy consumed during the driving cycle and
driving time are found out. Then a constant power source that could be used to produce all
energy consumed during the driving time can be calculated as in Equation (5.1). Where, E
presents the energy consumption and T is the driving cycle time. In this case, the constant
power source is considered the maximum allowable ICE/HPM generator output power in
SHEV configuration as shown in Equation (5.2).
tconsP
hrT
WhEtan)(
)( = (5.1)
PP ICEPMtcons (max)tan == (5.2)
Further, the peak power requirements by the traction motor during this driving cycle are
directly obtained from driving cycle power demand waveform. Then the minimum battery
power requirements to provide vehicle total power demand is calculated as in Equation
(5.3).
(max)(min) ICEPMpeakbat PPP −= (5.3)
Where, Pbat(min) presents the minimum battery power needed in the SHEV, Ppeak is the
driving cycle peak power demand and PICE/HPM(max) is the maximum allowable
engine/generator output power. Thus, from the previous discussion, the percentage HR
can be based on the battery and ICE/HPM generator full ratings as calculated as in
Equation (5.4).
( )bat
bat ICE / HPM(max)
P%HR . 100
P P= +
(5.4)
138
5.3 Proposed Power-train Applying that concept in series hybrid vehicle, a small engine is used to rotate a generator
at a constant, efficient speed and power output level. This set is combined with battery
system.
In a series hybrid vehicle, illustrated in Fig. 5.3, the ICE is downsized power capability to
operate the ICE engine over the most optimum region of the engine power-speed
characteristic in terms of specific fuel consumption and emissions.
Fig. 5.3 A series hybrid vehicle.
Generally, ICE’s demonstrate low specific fuel consumption over a relatively small region
of their engine power-speed characteristic. They also demonstrate particularly high fuel
consumption and emissions during transient engine operation. Therefore, mechanically
decoupling the ICE from the vehicle power-train (and hence road speed) and operating the
engine at a fixed speed and output power offers the potential for reduced engine size, fuel
and emissions reduction.
Brushless permanent magnet generators having a secondary source of excitation are an
interesting topology for implementation in a series hybrid vehicle; it is used in this
topology as an auxiliary power unit. Because of their duel excitation these machines are
referred to as hybrid permanent magnet (HPM) generators [149, 150]. The HPM machine
is a direct engine mounted generator in a series hybrid electric vehicle and is used to
optimise the ICE size and performance by maintaining a fixed ICE power output via the
HPM excitation field control scheme proposed in [149]. Hence, the interest in on-board
auxiliary power units that would serve as an energy input to the vehicle power-train during
sub-urban or highway driving, providing the desired range extension function for urban all-
electric vehicles.
139
A combination of ZEBRA battery, as a main energy/power source, and ICE, as an
auxiliary source, is one of the combinations to achieve a high efficiency system. ZEBRA
provides the dynamic power while ICE provides a constant power. In this kind of
configuration, the whole system efficiency will be improved because the battery system
captures all regenerative energy and the auxiliary source operates at optimum operation.
In this Chapter, a typical 2.5 tonne taxi is considered as the reference vehicle, the power-
train of which is studied in terms of maximum driving range per full fuel tank, fuel
consumption and emissions for prescribed inner city and highway driving routs in the UK.
ZEBRA is considered in the proposed series hybrid electric vehicle power-train to
supplement the vehicle with the all-electric energy during city driving, since there is an
increasing trend in several major European cities to designate parts of the downtown areas
as "emission free" zones due to vehicular derived airborne emission problems.
A detailed analysis of series hybrid power-train that consists of dual power and energy
source is required in order to optimise component specifications and energy management
strategies. For example, it is recognized in the literature [150-155] that, different energy
source combinations in EV’s and series hybrid electric vehicle’s have been analysed and
optimised for desired operating conditions. Such that, Zhancheng et al [151] summarizes
different power control strategies for HEVs and presents an approach to optimise the
operation of series hybrid electric vehicles for fuel economy and emissions based on the
forecasting of future load demands.
Unlike some of the reviewed papers, HEV power management implementing engine start-
stop routines is not considered in this Chapter. Here the focus is on the series hybrid
electric vehicle’s performance and driving range extension while maintaining minimal
vehicle fuel consumption and emissions by an optimised and downsized ICE/HPM
component that represents the auxiliary power unit. In this Chapter, several case studies
and simulation results for different driving cycles are investigated.
The vehicle examined is a functional equivalent of a typical London taxi. The propulsion
system evaluated here consists of:
• A ZEBRA battery (Z5-557-ML3C-32) as a stand alone energy source in an all-
electric configuration, and
140
• The ZEBRA battery, which presents the primary energy source, with a secondary
source comprised of an ICE/HPM generator system, fuelled by gasoline and used
mainly as a constant energy input to the vehicle power-train, essentially acting in a
range extending function in a series hybrid configuration format, as illustrated in
Fig. 5.4.
Fig. 5.4 The vehicle power-train in a series hybrid configuration format.
5.4 Series hybrid electric vehicle dynamic model representation The high level vehicle model, which illustrated in chapter 2, is used to investigate the
combination of ZEBRA battery and ICE/HPM as a range extender.
5.4.1 An ICE model In this Chapter, ICE is modelled to operate with a generator to be used as auxiliary energy
source in environmentally healthy operation. For this function, the ICE model is proposed
as efficiency map and implemented in Simulink/ Matlab environment. All parameters in
terms of gas emission and efficiency are predicted.
ICE efficiency map is simulated using data for Toyota Prius 1.5l (43kW) ICE stored in a
look up table of fuel consumption, efficiency, and gases emission. All data are in terms of
torque (Nm) and angular velocity (rad/s). The speed is chosen from the driving cycle’s
library, while the torque is calculated from the speed profile in the torque equation.
To investigate any ICE application using vehicle model, ICE size could be scaled up or
down to the required size. The ICE model is illustrated in Fig. 5.5; the inputs data of in this
case are generated from NEDC speed profile.
141
(a) Simulink model
(b) Implementation of ICE Look up tables.
Fig. 5.5 Simulink ICE model.
The results of ICE efficiency and fuel consumption over NEDC driving cycle are shown in
Fig. 5.6, it is clear that the efficiency is very low and it changed not more 35%, the
efficiency is depends on the demand power, torque, and the linear velocity, which are the
142
input. As a result of low efficiency, high fuel consumption is consumed and high rate of
emission, as shown in Fig. 5.6(b).
0 500 1000 1500 2000 25000.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Time (s)
Effi
cien
cy (
%)
(a) ICE Efficiency.
0 500 1000 1500 2000 25000
0.5
1
1.5
2
2.5
3
Time (s)
Fu
el u
sed
(g
/s)
(b) ICE Fuel consumption.
Fig. 5.6 Example of ICE model outputs over typical NEDC driving cycle.
143
5.4.2 The optimisation of ICE
To optimise the engine performance in terms of fuel consumption and gas emission, the
engine is operated at a fixed speed of 3000rpm (314 rad/s) chosen, in this case, to minimize
the ICE emissions for a constant output power, as illustrated in Fig. 5.7 showing ICE CO2
emissions for varying engine load torque and speed. The average fuel consumption and
emissions of the ICE hybrid were determined by scaling the fuel consumption and
emissions of the Toyota Prius 43 kW engine to the desired downsized ICE rating based on
the calculated peak power requirements calculated for the proposed driving cycles.
.
.
.. .
T=1 Nm
T=11 Nm
Operating line
0
5
10
15
20
25
30
50 100 150 200 250 300 350 400 450 500
Speed, rad/s
Car
bo
n d
ioxi
de,
mg
/s
Fig. 5.7 Engine emission for different torques.
5.4.3 Power-train Efficiency consideration
The prime mover for the taxi is a brushless permanent magnet (BLPM) machine with an
integrated gear reduction and differential drive to the vehicle back-axle. The BLPM
machine is controlled via a three phase voltage source converter, the DC supply to which is
144
provided by the traction battery and ICE/HPM generator combination. In order to evaluate
the performance of the vehicle model under different modes of operation, the losses of all
components in the power-train are calculated from during the vehicle simulation.
The traction machine operates as a motor during acceleration or as a generator during
regenerative mode. The efficiency is calculated from an efficiency map of the complete
machine power converter embedded as a Look-up table in the traction system model, as
illustrated in Fig. 5.8.
Fig. 5.8 Matlab/Simulink traction machine dynamic model.
5.5 Case studies and simulation results Before starting the analysis on both EV and SHEV power-train, a benchmark data of pure
conventional vehicles for urban, sub-urban and combined driving cycles of different
generations have been obtained. There are many generation of LTI taxi sited in the internet
[156], in which, some of the different generations and some actual data that have been
gathered from different taxi drivers are listed in Table 5.1 and 5.2. The new generation is
made 2006, where the old one is 2001. Moreover, the collected data in Table 5.1 is
compared with numerical test results, via the proposed vehicle power-train simulation
platform, of a scaled up (75 kW) ICE, which supports vehicle dynamic model accuracy.
The vehicle dynamic model simulation results predicts fuel consumption in mile per UK
gallon (m/g) and CO2 emission which shows to be very close to the actual taxi data, as in
Table 5.1.
145
Driving cycle
Old generation (Auto)
New generation (Auto)
New gen. (Manual)
Urban 25.3 25.5 28.0
Sub- urban
35.1 38.2 41.5
Fuel consumption
(m/g)
combined 30.7 32.0 36.2
CO2 Emission (g/km)
Sub- urban
243
233
211
Table 5.1 London taxi LTI TX4 specification of two made
(Auto and Manual) over NEDC.
Driving cycle
Actual data (2005-2009)
Scaled ICE (75 kW Max.)
Urban 23.0 23.5
Sub- urban
27.1 28.0 Fuel consumption
(m/g)
combined 25.0 25.9
CO2 Emission (g/km)
Sub- urban
--
267
Table 5.2 Actual and scaled 75kW Toyota Prius over NEDC.
The reference or base point for these case studies is two typical driving regimes that may
be called on by a taxi in everyday operation:
• Manchester inner city driving characterised by ECE15 driving cycle.
• Motorway driving where an example destination of Manchester to London
(equating to 335 km) is chosen.
The assumption is that the taxi generally operates on inner city routes that could be
facilitated by all-electric operation, but that occasionally an inter-city route is required
demanding a higher energy usage than would normally be experienced. How then will the
power-trains of the future be augmented to satisfy these competing challenges is the
question to be addressed.
For the specified series hybrid vehicle power-train, a production penetration scenario is
needed to asses the adequacy of the calculated ICE/HPM generator size.
146
The vehicle assessment is based on vehicle driving range, battery SOC, specific fuel
consumption and emissions, by the taxi, for the two driving cycles. Note that the
degradation of ICE function with time is ignored in this assessment.
Repetitive cycling of the sub-urban part of the New European Driving Cycle (NEDC) and
US Highway driving cycle (HWFET) were the chosen duty profiles used in the simulation
study, as illustrated for reference in Fig. 5.9 respectively. Table 5.3 summarizes the
specifications of the proposed cycles, highlighting the wide disparity in peak-to-average
power requirements.
4X ECE15
Sub-urban
0
10
20
30
40
0 100 200 300 400 500 600 700 800 900 1000 11001200
Time, s
Sp
eed
, m/s
(a) Sub-urban part of the NEDC
0
10
20
30
0 200 400 600 800
Time, s
Sp
eed
, m/s
(b) US Highway driving cycle (HWFET)
Fig. 5.9 Reference duty profiles for the simulation study.
147
Driving cycle
profile
Re-gen. braking
Cycle Time (s)
Average power (kW)
Peak power (kW)
Yes 17.8 NEDC Sub-urban No
400 18.4
63.2
Yes 20.5 HWFET
No 765
20.8 62.0
Yes 5.81 ECE 15
No 195
6.07 35.1
Table 5.3 Summary of power requirements for proposed vehicle driving profiles.
Fro sub-urban driving cycle, as illustrated in Table 5.3, the peak power required to the
traction motor during this driving cycle is about 63.2 kW.
Using the simulation, the energy consumed during the driving cycle and driven time are
found out. Then a constant power source that could be used to produce all energy
consumed during the driven time can be calculated using equation 5.1.
In this case, the constant power source will be considered as a maximum ICE PM Power,
in case of sub-urban NEDC , applying equation 5.2,
kWPP ICEPMtcons 18(max)tan ==
Thus, the minimum battery power required to provide the total power is calculated using
equation 5.3,
kWPbat 2.45182.63(min) =−=
From this calculation and based on the discussion earlier for ICE and battery combination,
ICE to provide a constant power where the battery system to provide the dynamic power,
and to move out from all electric to hybrid, the limits of battery power is between 64kW
(total of two ZEBRA batteries) and the minimum limit (45.2kW), as shown in Fig. 5.10,
and ICE size limits between zero at all electric vehicle to 18kW.
Fro US highway driving cycle: as illustrated in Table 5.3, the peak power required to the
traction motor during this driving cycle is about 62 kW.
In US highway HFET driving cycle case,
kWPP ICEPMtcons 20(max)tan ==
148
Thus: the minimum battery power required to provide the total power is calculated using
equation 5.3,
kWPbat 422062(min) =−=
likewise, the limits of battery power is between 64kW (total of two ZEBRA batteries) and
the minimum limit (42kW) ,as shown in Fig. 5.10, and ICE size limits between zero at all
electric vehicle to 20kW.
Hence, for the specified series hybrid vehicle power-train, a production penetration
scenario is needed to asses the adequacy of the calculated ICE/HPM generator size. Based
on sub-urban NEDC and HWFET, the maximum allowable ICE/HPM generator output
power in the proposed SHEV power-train configuration are 18, 20 kW respectively.
Therefore, three case studies are considered, in here, each with two operating scenarios.
Case 1 and 3 presents a comparison of a pure EV mode, i.e. ZEBRA battery only, and a
hybrid mode comprising of the ZEBRA battery and a 3kW ICE/HPM generator system.
Case 2 presents a comparison of a hybrid mode with a larger ICE/HPM generator system,
which attains the desired minimum range of driving between some destinations, i.e. a
ZEBRA battery and a 14.55 kW ICE/HPM generator system that is scaled from the 3 kW
unit have been selected. For reference, a pure EV is considered where the ZEBRA battery
system has an increased mass equivalent to the 14.55 kW ICE/HPM generator and 2/3 of
the full fuel tank mass. Fig. 5.10 shows the HR due to 3 and 14.5 kW ICE/HPM
generators.
Fig. 5.10 Hybridization Ratio plot for Case 1 and 2.
64 0 48 16 32 32 16 48 0 64 100 80 60 40 20 0
Bat
tery
pow
er, k
W IC
E/H
PM
Pow
er, kW
% Hybridisation ratio
3 kW ICE/HPM 14.55 kW ICE/HPM Max. allowable ICE/HPM power
149
5.5.1 Simulation results
Vehicle fuel consumption (VFC) is simply calculated as in Equation (5.5). Where, FCR
represents the fuel consumption rate, ICEP the input power of the HPM generator (which is
calculated by the addition of the HPM generator output power and its stator and rotor
losses), t is the instantaneous driving time, ICEη is the ICE efficiency and EC the energy
content of gasoline in consumed fuel rate.
EC
tPFCR
VFC ICE
ICE
=η
(5.5)
Then, vehicle fuel consumption is used to calculate the mass of the fuel tank (kg) using the
litre to gram conversion factor which is 737.22 for gasoline. The mass is based on the
maximum fuel capacity of the vehicle fuel tank which is 15 litres. Simulation results of the
study cases are shown in Tables 5.4 and 5.5. The results illustrates that driving range can
be extended by 18% and 23% for HWFET and NEDC cycles, respectively, in case 1; and
the driving range can be extended by 34% and 52% for HWEFT and NEDC, respectively,
in case 2. However, using the combination of case 1, the vehicle could not reach the
proposed destination, but the energy used was better than the pure battery scenario. In case
2, where equal energy storage mass for both scenarios was taken into account, the new
rating of the ICE/HPM generator system in the proposed taxi power-train reached the
specified destination and passed it by 102 km with less CO2 emission, comparing to the
European Commission proposal for 2012 which is 120g/km [157]. A better utilization of
the battery SOC, i.e. down to 0.1, is achieved by the NEDC cycles of scenario 2 in case 2 if
it were to be compared with 0.149 as in NEDC cycles of scenario 2 in case 1. Moreover,
stored energy in the proposed combination was used efficiently for both driving cycles.
150
Scenario 1 Scenario 2
Case 1 Pure EV mode Hybrid mode ZEBRA + 3kW HPM
Driving cycles HWFET
NEDC
Sub-Urban
HWFET
NEDC
Sub-Urban
Range (km) 103 89.3 121.8 110.3 Fuel use (l) 0 0 1.566 1.721
Fuel use (l/km) 0 0 0.0128 0.0156 SOC 0.1 0.177 0.1 0.149
CO (g/km) 0 0 0.56 0.65 CO2 (g/km) 0 0 25.3 30.14 HC (g/km) 0 0 0.136 0.159 NO (g/km) 0 0 0.265 0.31
Notes: The energy content of gasoline in 1 litre = 32.2 MJ and The gram to litre conversion factor = 737.22.
Table 5.4 Comparison of the simulation results between pure electric and hybrid vehicle modes of Case 1.
Scenario 1 Scenario 2
Case 1 Pure EV mode Hybrid mode ZEBRA + 14.5kW HPM
Driving cycles HWFET
NEDC
Sub-Urban
HWFET
NEDC
Sub-Urban
Range (km) 154.3 124.2 361.9 437.5 Fuel use (l) 0 0 21.7 30.93
Fuel use (l/km) 0 0 0.06 0.0707 SOC 0.1 0.24 0.1 0.1
CO (g/km) 0 0 1.87 2.2 CO2 (g/km) 0 0 86.5 102 HC (g/km) 0 0 0.456 0.537 NO (g/km) 0 0 0.89 1.05
Table 5.5 Comparison of the simulation results between pure electric and hybrid vehicle
modes of Case 2.
Furthermore, Fig.5.11 shows how the total power demand positive peaks decreases (in
generation mode) which means that the ICE/HPM share the demand power and how the
total power demand negative peaks increases (in regeneration mode) which means that the
ICE/HPM charge the battery in case of no demand to the load, this gives a better battery
utilization due to the inclusion of ICE/HPM generator constant power supply. Moreover,
the terminal voltage variation is less due to the proposed ICE/HPM generator size in
SHEV, as result of that the driving range of the proposed combination is extended.
151
0 100 200 300 400 500 600 700 800 900 1000
-10
0
10
20
30
40
50
Time (s)
Po
wer
(kW
)
Total powerICE/HPM powerBattery power
Fig. 5.11 Power demand from each energy source component.
The inner city driving for pure electric and ZEBRA + 3kW ICE/HPM generator results are
illustrated in Table 5.6. The results shows that there is a 100% driving range improvement
in the hybrid case mode, however, since the engine is in operation for the whole period of
the driving cycle, and even at zero speed, the fuel consumption increased by almost a
factor of six if it was to be compared with fuel consumption of the sub-urban driving cycle
cases.
152
Scenario 1 Scenario 2 Case 3 Pure EV mode
ZEBRA only Hybrid mode
Hybridization ratio=95.5 ZEBRA + 3kW HPM
Driving cycles ECE 15 ECE 15
Range (km) 92.01 183.4
Fuel use (l) 0 9.677
Fuel use (l/km) 0 0.0528
Fuel use (m/g) 0 53.5
SOC 0.1 0.1
CO (g/km) 0 2.29
CO2 (g/km) 0 105.45
HC (g/km) 0 0.558
NO (g/km) 0 1.089
Table 5.6 Comparison of the simulation results between pure electric and hybrid vehicle modes
of Case 3(inner city driving ECE 15).
5.5.2 Comparison between hybrid and conventional vehicle
Fig. 5.12 illustrates in a summarized way the comparison between equal masses of four
energy systems considering the all-electric, hybrid (3 and 14.5 kW), and pure conventional
vehicle case over sub-urban NEDC in terms of range, emission, and fuel economy. For
pure conventional vehicle the emission equals 267 g/km, where for hybrid vehicle cases it
equals 26.56 and 102 g/km for 3 and 14.5 kW ICE/HPM generator systems respectively. In
the 14.5 kW ICE/HPM generator case, the fuel economy and range shows a better results
than the pure conventional case due to the optimized down scaled engine. Moreover, in
terms of range improvement, for a similar fuel tank capacity, the 14.5 kW ICE/HPM
hybrid vehicle case the driving range reaches 430 km, where for the 75 kW pure
conventional vehicle the driving range equals 300 km. Hence, a greater driving range have
been achieved by the inclusion of a small engine in the EV with the appearance of a small
amount of emission if it was to be compared with the pure electric case, however this
emission is almost one third the pure conventional vehicle case.
153
0
60
120
180
240
0102030405060708090100
All-electric Hybridsation ratio, % Conventional
Range, km
; C
arbon d
ioxi
de, g/k
m
0
80
160
240
320
400
480
Fuel e
conom
y, m
pg
Fuel economy (mpg) Carbon dioxide emission
Range (km) EU CO2 emission standred, 2012
Fig. 5.12 Comparison between the all electric, hybrid (3 kW, 14.5 kW), and pure conventional vehicle Cases over Sub-Urban NEDC.
5.6 Summary A detailed dynamic model of series hybrid electric vehicle powered by a combination of
battery and ICE/HPM generator systems, has been used to investigate all- and hybrid-
electric vehicle operation via case studies based on a typical urban 2.5 tonne taxi. In the
taxi power-train, a ZEBRA battery presents the main energy source while an ICE based on
a downsized Toyota Prius engine and HPM forms an auxiliary energy source to provide the
desired range extension function. Different scenarios are discussed for the optimal
operation of the vehicle on the road to asses the adequacy of the calculated ICE/HPM
generator size. The assessment is based on vehicle driving range, battery SOC, ICE
specific fuel consumption and emissions for two driving cycles, inner city and
suburban/motorway. The simulation model is used to determine a minimum ICE/HPM
generator rating which, for the cases considered, equated to 14.55 kW. Thus, by
employing the 14.55 kW ICE/HPM generator, as in scenario 2 of case 2, the simulation
results suggest that the proposed combination can fulfilled the requirements of emissions
lower than the 2012 European Commission proposal. In addition, the battery energy is
used more efficiently and an improved battery life is suggested by virtue of the reduced
154
transient battery loadings. Hence, for engine assist hybrid electric vehicles in series
power-train configurations, existing ICE technology and efficiency can reduce CO2
emissions per km while satisfying the desired range extension functions detailed in the
considered case studies.
155
CHAPTER 6
CONCLUSIONS AND RECOMMENDATION FOR
FURTHER WORK
6.1 Conclusions
Comparing with internal combustion engine (ICE) vehicles, Electric vehicles (EV’s)
powered by electro-chemical batteries are more reliable, safe and better quality in terms of
performance and energy conversion efficiency, moreover, they are environmentally free of
emissions. The environmental issues and concerns over sustainable energy use are the key
motivating factors in the development of alternative energy sources and power-trains for
road vehicles. These early advantages quickly subsided with the internal combustion
engine (ICE) which addressed the range issue that was, and still is, problematic with
electric vehicles. The implementation of more than one energy source in an electric vehicle
power-train is to elevate the problems faced by the limited energy density of existing state-
of-art electro-chemical batteries. In this study, some power-train configurations of hybrid-
electric vehicles have been proposed, modelled and then investigated.
The proposed models of energy system components are integrated into a complete vehicle
model that will approximate the behaviour of the system of the interest. The model showed
156
how component choices affect the overall performance of the system beyond the particular
aspect that motivated the selection. The system model suggested a set of combinations by
selecting and sizing and configuring the components, and then optimises the control
scheme to get the performance requirements.
There are many options of energy source combinations for electric vehicles, in this study
ZEBRA battery is chosen to be modelled as energy dense source, supercapacitor is
simulated as a part of the model to present the power dense, ICE and generator set also is
modelled and tested as energy extended for the main energy dense.
The look-up table ZEBRA model is validated by measured data provided by the
DESERVE project, the model showed an excellent agreement with the measured result.
Regardless to other batteries, in ZEBRA battery the cell failed to a short circuit which
makes the string can continue to operate; this feature makes ZEBRA battery is very robust.
In Chapter 3, the multi-battery system has been modelled. The model showed the
interconnection between the ZEBRA batteries and how the batteries share current in
different cases. The MBS is simulated manages the unbalanced system to maintain vehicle
functionality by disconnecting the faulty battery. The real field data of four batteries
system has been used to valid the model. The model showed a good agreement with the
real data. The performance of the multiple battery systems in the case of various unbalance
operating scenarios hence are analysed and investigated.
In Chapter 4, the supercapacitor is simulated as nonlinear voltage dependent capacitance,
the model is validated. The impact of temperature on supercapacitor bank is simulated and
tested in the laboratory. The control strategy was based on that the recovered energy,
which transferred to the supercapacitor proposed and designed. Based on the vehicle
model, the supercapacitor system for 3500 kg van over the ECE15 driving cycle is sized.
As a result, the battery mass is downsized by approximately 17%.
At a fixed mass of battery, the performance of the vehicle is improved by:
• Increasing battery life by decreasing battery current fluctuation, which means
decreasing discharge/ charge current that drawn from the battery.
• The range is extended by about 24%.
• Better energy utilisation.
• Reducing the voltage variation on the battery voltage that makes the inverter design
simpler and less loss.
157
In Chapter 5, a detailed dynamic model of series hybrid electric vehicle powered by a
combination of battery and ICE/HPM generator systems, has been used to investigate all-
and hybrid-electric vehicle operation via case studies based on a typical urban 2.5 tonne
taxi. In the taxi power-train, a ZEBRA battery presents the main energy source while an
ICE based on a downsized Toyota Prius engine and HPM forms an auxiliary energy source
to provide the desired range extension function. Different scenarios are discussed for the
optimal operation of the vehicle on the road to asses the adequacy of the calculated
ICE/HPM generator size. The assessment is based on vehicle driving range, battery SOC,
ICE specific fuel consumption and emissions for two driving cycles, inner city and
suburban/motorway.
The simulation model is used to determine a minimum ICE/HPM generator rating which,
for the cases considered, equated to 14.55 kW. Thus, by employing the 14.55 kW
ICE/HPM generator in the model, the proposed combination fulfilled the requirements of
emissions lower than the 2012 European Commission proposal. In addition, the battery
energy is used more efficiently and an improved battery life is suggested by virtue of the
reduced transient battery loadings. Hence, for engine assist hybrid electric vehicles in
series power-train configurations, existing ICE technology and efficiency can reduce CO2
emissions per km while satisfying the desired range extension functions detailed in the
considered case studies.
6.2 Contribution
In this project, a complete all electric and hybrid electric vehicles has been implemented
and validated over real driving cycles. This work develops a model for a multi-battery
system (MBS) to study unbalanced operation and investigate it’s impact on vehicle
performance in terms of range and energy use.
This analysis also develops a Maxwell supercapacitor model using a variable non-linear
capacitance function determined from test, and implementing a thermal model as part of
the full cell model. The test of three 48V modules connected in series was carried out over
a dynamic regime common for EV applications. The results show good correlation
between calculated and measured supercapacitor input-output energies, as well as
calculated and measured temperatures.
6.3 Publications arising from this thesis study
The following is a list of conference and journal publications that resulted from this study:
158
1 Jarushi, A. M., Schofield, N.: “Modelling and analysis of energy source
combinations for electric vehicles”, World Electric Vehicle Journal, Vol. 3, Paper
5940531, pp. 1-7, 2009, ISSN 2032-6653.
2 Jarushi A., Schofield N.: “Modelling and Analysis of Energy Source
Combinations for Electric Vehicles”, EVS24, Stavanger, Norway, May 13-16,
2009.
3 Jarushi A., Schofield N.: “Multiple Battery Systems for Electric Vehicles”,
EVS24, Stavanger, Norway, May 13-16, 2009.
4 Jarushi, A. M.; Schofield, N.: "Battery and supercapacitor combination for a series
hybrid electric vehicle", 5th IET International Conference on Power Electronics,
Machines and Drives (PEMD 2010), pp.1-6, 19-21 April 2010.
5 Al-Adsani A., Jarushi A., Schofield N.: “Operation of a Hybrid PM Generator in a
Series Hybrid Electric Vehicle”, The 35th Annual Conference of the IEEE
Industrial Electronic Society (IECON 2009), Porto, Portugal, 3-5 Nov. 2009.
6 Al-Adsani A., Jarushi A., Schofield N.: “An ICE/HPM generator range extender
in a series hybrid electric vehicle”, 5th IET International Conference on Power
Electronics, Machines and Drives (PEMD 2010), pp.1-6, 19-21 April 2010.
7 Al-Adsani A., Jarushi A., Schofield N.: “An ICE/HPM generator range extender
in a series hybrid electric vehicle”, submitted to IET Journal, 2010.
6.4 Recommendations for further work
Although the vehicle models with proposed combinations of energy sources have been
successfully implemented and investigated, some recommendations are useful in order to
improve the model performance. The ZEBRA model developed in this work could be
modified by introducing a thermal model and this would result in improving the overall
model resolution. Further work on developing a more advanced model that would
incorporate other battery technologies, like the LiIon battery, and thus facilitate
comparison between a wider range of technologies in terms of energy and efficiency,
would be beneficial, and lead to an improvement in identifying the optimal vehicle energy
source combination.
.
159
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