«emr of battery and traction systems - emr of...08/07/2016 3 emr’16, udes longueuil, june 20165...

08/07/2016

1

EMR’16

UdeS - Longueuil

June 2016

Summer School EMR’16

“Energetic Macroscopic Representation”

«EMR OF BATTERY AND TRACTION

SYSTEMS»

Nicolas Solis12, Luis Silva1 , Dr. Ronan German2,Pr. Alain Bouscayrol2

1 Université de Rio Cuarto, Argentina2 L2EP, Université Lille1, MEGEVH network, France

EMR’16, UdeS Longueuil, June 20162

« EMR of battery and traction systems»

- Outline -

1. Context of the presentation

• Description of the work

• Batteries in EV context

• Importance of temperature for battery

2. Battery modeling

• Electrical model of battery

• Thermal model of battery

• Coupling thermal and electrical domains by EMR

3. Simulation results

• Validation model with literature results

• Interest on temperature estimation (WLTC Cycle)

• Interest on SOC estimation (WLTC Cycle)

4. Conclusions

08/07/2016

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EMR’16

UdeS - Longueuil

June 2016



« CONTEXT OF THE PRESENTATION »



- Description of the work-

Goal of the work

Take into account temperature in battery models

Couple existing electric and thermal models

Method

Application

Energy management of batteries (in EV for example ….)

Ibat cyclesNormalized

speed cycles

UAC

WLTC

Tazzari Zero

model

M=542 kg

P= 14,5kW

LiFePo high

power cells model

Cbat=2.5 Ah

Ibat max= 20 C0 500 1000 1500-50

0

50

100

Time (s)

i ba

t (c-r

ate

)

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- EV related definitions-

Electrical vehicles (EV)

• Less maintenance than ICE vehicles

• No direct emissions of CO2

• Higher efficiency (80%) compared to ICE vehicles (40%)

• Vehicles propulsed only by electric energy without the help of any ICE

EV strong points

ESS available for EV

Li-ion Batteries pack

Energy storage systems

• Store and give back energy

SC module Fuell cell + H2 tank Capacitors

The L2EP, Tazzari Zero



- Batteries in EV context-

• Responsible of

• Cost

• Recharge time

• Autonomy of the vehicle

Comparison of different ESSs

100

102

104

106

10-2

100

102

104

Mass Power (W/kg)

Mas

s En

ergy

(W

h/k

g)

36 ms

1 h 36 s100 h

Fuell cell

SCs

Capacitors

Li-ion battery technology

• Energy density compatible

with 150 km autonomy for

standard EV

• Power density compatible

with EV acceleration

Batteries

Li-ion

Ni-MhPb

In most EV the battery is the main ESS,

Example of 14,5 kWh Li-ion pack

placed in theTazzari Zero

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- Importance of temperature for battery-

i

e-e-

Electrodes

SeparatorElectrolyte

Solid Lithium Ionic Lithium

e- Electrons flow

Energy storage principle

Instant battery electric parameters

variation

• Increase of Electrolyte viscosity

with lower temperature

Ageing acceleration factor

Catastrophic fails

Triple temperature effect on batteries

• Fast breakdowns for out of bounds

temperature (Low or High)

Rbat dependent of T°

Cbat dependent of T°



- Quantification of temperature impact on batteries-

Results on battery electric parameters variation with temperature*

[Lin et al 13]

0 20 40 60 800.005

0.01

0.015

0.02

0.025

0.03

Rbat

(mΩ)

T (ºC)

Cbat

(P.U)

[ Results extracted from

Jaguemont 14]

-20 0 20 40

0.8

1

1.2

1.4

T (ºC)

Temperature in battery modelling is very important

Results on battery ageing with temperature

*Results obtained for LiFePo batteries

+10 °C Life time reduced by half [ Edd 12]

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EMR’16

UdeS - Longueuil

June 2016



« BATTERY MODELLING »



- Battery equations and EMR representation in electrical domain-

Usual Battery and traction EMR representation

• EMR representation

UOCV

ibat

Rbat

Ubat

Battery Converter of

EV

traction

Uconv

iconv

BattEV

Traction

Ubat

ibat

• Electrical domain only

Other possible battery EMR representation

• EMR representation

[Lin et al 2013]

OCV

storage

UOCV

ibatRbat

UbatUOCV

ibat

OCV

Storageibat

Ubat

[ Bouscayrol 12]

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- Battery equations and EMR representation in thermal domain

Power loss in Rbat is the power heat source

[Lin et al 2013]

• Thermal domain modelling :

• Kinetic Variable=𝑞𝑠𝑥 (Entropic Flow)

• Potential Variable=𝑇𝑥 (Temperature)

Simplified EMR representation of the thermal domain of the

battery

Tcoren

qs1

Heat

Source

Heat Power

Source

Thermal

model

UOCV

ibat

Rbat

Ubat

Pheat = Pjoule= Rbat ibat2

Pheat=qs1Tcore

Pheat=qsxTx



- Introduction to battery thermal modeling-

Thermal capacitance

Thermal energy storage

Thermal resistance

Selfheating as a function of the power transfert

Hypothesis• Heat source at the core center

• Radial conduction only in solid

• Convection only for solid to gas

heat transfer

• Only contact thermal resistance

taken into account

Package

Surface

Important notions

Heat

power

source

Tamb

Ccore Csurf

Rcond Rconv

Tsurf

Tamb

Pheat=RBat.IBat² TCore

Equivalent circuit

thermal model

Battery (1cell)

Core

Air

Tamb

Air

Tamb

[ Forgez 09] [Lin 13]

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- Battery equations and EMR representation in thermal domain-

Heat

power

source

Tamb

P2 P4

Ccore Csurf

Rcond Rconv

Tsurf

Tamb

Pheat=qs1Tcore

Equivalent thermal model(structural representation)

Electrical parameters are usually fixed

Ccore RconvRcond

Tcore

Tamb

EnvHeat

SourceTcore

qs1

qs3

qs4 qs7Tsurf

qs6

Csurf

Tsurf

Equivalent thermal model(EMR representation)



- Coupling of electrical and thermal model of battery with EMR-

• Electrical domain

Ubat=UOCV - ibatRbat

• Thermal domain

Tcoren

qs1 Heat

Source

qs1=Pheat/TcorePheat= ibat2 Rbat

Ubat

Electro-thermal

coupling

UOCV

ibat

ibat

Tcore

qs1

EV

Traction

Thermal

EMR

OCV

Storage

Rbat

Rbat

Rbat in the coupling element in EMR

Rbat is the common element

Batt

UOCV

ibat

Ubat

ibat

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- Coupling of battery in EMR-

Final thermo-electric battery representation

Worthy to have this complexity?

• Electrical and thermal models coupled with EMR

• With temperature dependent electrical parameters

𝐶𝐵𝑎𝑡 = 𝐶𝐵𝑎𝑡 0 + 𝐾𝐶 ∙ 𝑇𝐶𝑜𝑟𝑒𝑅𝐵𝑎𝑡 = 𝑅𝐵𝑎𝑡 0 ∙ 𝑒−𝑇𝐶𝑜𝑟𝑒𝑇0

UOCVOCV

storageibat

EV

Tractionibat

Ubat

Tcore

Tcore

Ccore

Tsurf Tamb

Env

RconvRcond

qs1

qs3

qs4 qs7Tsurf

qs6

Csurf

EMR’16

UdeS - Longueuil

June 2016



« SIMULATION RESULTS»

08/07/2016

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- Validation of the model with temperature dependence-

• Drive cycle: UAC

0 500 1000-20

0

20

Time (s)

i ba

t (c-r

ate

)

• Good dynamic

• Maximum error in

temperature=1,5 ºC

• Tamb=25 ºC

• Battery = A123 systems LiFePO4

• 2,5 Ah

• 3,3 V

• 20 C

Model validated

0 200 400 600 800 100025

30

35

40

45

50

55

Time (s)

Tem

pera

ture

(ºC

)

Experimental results

from [Lin et al 2013]

CoreSimulation

Surface

ExperimentWith temperature dependence

Experimental setup for validation



- Classic and temperature dependent models comparison -

Necessity of thermal

depence model validated

• Tamb=25 ºC

• Battery modeled = A123 systems LiFePO4

• 2,5 Ah

• 3,3 V

• 20 C

0 500 1000 1500-50

0

50

100

Time (s)

i ba

t (c-r

ate

)

• Drive cycle: WLTC

• Maximum error in

temperature= 4 ºC

• Temperature over-estimated

Without temperature dependence

Temperature estimation (simulation)

0 500 1000 150025

30

35

40

Time (s)

Tem

pera

ture

(ºC

)

Core

Without T° dependence

With T° dependence

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Necessity of thermal

dependence model

validated

• Under-estimated SOC

• Maximum error in SOC = 8,4 %

0 500 1000 15000

20

40

60

80

100

Time (s)

SO

C (

%)

Without T° dependence

With T° dependence

0 500 1000 1500-50

0

50

100

Time (s)

i ba

t (c-r

ate

)

• Drive cycle: WLTC• Tamb=25 ºC


• 2,5 Ah

• 3,3 V

• 20 C

Without temperature dependence

SOC estimation (simulation)




0 500 1000 1500-50

0

50

100

Time (s)

i ba

t (c-r

ate

)

• Drive cycle: WLTC


• 2,5 Ah

• 3,3 V

• 20 C

Simulations at different temperatures

Errors on estimations without temperature dependence

Ambient T° (°C) 25 °C -20 °C

Max TCore

estimation error+4 °C -12 °C

Max SOC

estimation error-8.4 % + 10 %

08/07/2016

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EMR’16

UdeS - Longueuil

June 2016



« Conclusions»



Conclusions and perspectives

Temperature dependent on battery electrical parameters is necessary

• Better estimation of SOC

• Better estimation of temperature

Battery are the key component of the majority of EV

Battery physical principles explains T° dependence

EMR allows easy organization for coupling different physical domains

08/07/2016

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EMR’16

UdeS - Longueuil

June 2016



« BIOGRAPHIES AND REFERENCES »



- Authors -

Dr. Ronan German

University Lille 1, L2EP, MEGEVH, France

PhD in Electrical Engineering at University of Lyon (2013)

Research topics: Energy Storage Systems, EMR, HIL simulation,

EVs and HEVs

Angel Nicolas SOLIS

University Lille 1, L2EP, France

Ing in Electrical Engineering at Univ.National of Río

Cuarto (2014)

Research topics: EMR, EVs

08/07/2016

13



Prof. Alain BOUSCAYROL

Université Lille 1, L2EP, MEGEVH, France

Coordinator of MEGEVH, French network on HEVs

PhD in Electrical Engineering at University of Toulouse (1995)

Research topics: EMR, HIL simulation, tractions systems, EVs and HEVs

- Authors -

Dr. Luis Silva

Universidad Nacional de Rio Cuarto, GEA, Argentina

PhD in Sciences of Engineering at UNRC (2012)

Research topics: EMR, Modeling and Simulation of

Electric and Hybrid Vehicles



- References -

[Bouscayrol 12] A. Bouscayrol, J. P. Hautier, B. Lemaire-Semail, "Graphic formalism for the control of

multi-physical energetic systems", Systemic design methodologies for electrical energy, tome 1, Chapter 3,

ISTE Willey editions, October 2012, ISBN 9781848213883

[Lin 13] X. Lin, H. E. Perez, S. Mohan, J. B. Siegel, A. G. Stefanopoulou, Y. Ding, M. P. Castanier, “A

lumped-parameter electro-thermal model for cylindrical batteries”, Journal of Power Sources, Volume 257,

1 July 2014, Pages 1-11, ISSN 0378-7753.

[Jaguemont 14] J. Jaguemont, L. Boulon, Y. Dube and D. Poudrier, "Low Temperature Discharge Cycle

Tests for a Lithium Ion Cell“, 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), Coimbra,

2014, pp. 1-6.

[Dürr 06] Matthias Dürr, Andrew Cruden, Sinclair Gair, J.R. McDonald, Dynamic model of a lead acid

battery for use in a domestic fuel cell system, Journal of Power Sources, Volume 161, Issue 2, 27 October

2006, Pages 1400-1411, ISSN 0378-7753, http://dx.doi.org/10.1016/j.jpowsour.2005.12.075.

[Forgez 09] Christophe Forgez, Dinh Vinh Do, Guy Friedrich, Mathieu Morcrette, Charles Delacourt,

Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery, Journal of Power Sources, Volume

195, Issue 9, 1 May 2010, Pages 2961-2968, ISSN 0378-7753,

http://dx.doi.org/10.1016/j.jpowsour.2009.10.105.

[Edd 12] A. Eddahech, O. Briat, E. Woirgard, J.M. Vinassa, Remaining useful life prediction of lithium

batteries in calendar ageing for automotive applications, Microelectronics Reliability, Volume 52, Issues 9–

10, September–October 2012, Pages 2438-2442.

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