Modeling and Simulation of a Series Parallel Hybrid ElectricVehicle UsingREVS

Reza Ghorbani, Eric Bibeau, Paul Zanetel and Athanassios KarlisDepartment of Mechanical and Manufacturing EngineeringUniversity of Manitoba, Winnipeg, MB, Canada R3T 5V6

Email: umghorba@cc.umanitoba.ca, ericbibeau@umanitoba.capaul zanetel@umanitoba.ca, akarlis@ee.duth.gr

Abstract— This paper discusses a simulation and modelingpackage developed at University of Manitoba, REVS: Renew-able Energy Vehicle Simulator. REVS assists in detail studiesof hybrid electric vehicles (HEV) and plug in HEV (PHEV)configurations or energy management strategies through visualprogramming by creating components as hierarchical subsys-tems. REVS is composed of detailed models of seven major typesof components: electric motors, internal combustion engines,batteries, chemical reactions, fuzzy control strategies, renewableenergy resources and support components that can be inte-grated to model and simulate hybrid drive trains in differentconfigurations. REVS was developed in the Matlab/Simulinkgraphical simulation language as well as IDEAS package andis portable to most computer platforms. A series parallel hybridelectric vehicle (HEV) with a conventional internal combustionengine (ICE) and a power split device have been designed usingthe simulation package. Fuzzy controllers have been developedto track the desired vehicle velocity and manage the energy flowof the vehicle. Simulation results of the energy managementsystem and dynamic responses of the system are discussed forproposed vehicle.

I. I NTRODUCTION

Transportation is almost exclusively based on the use ofnon-renewable fossil fuels. Electricity use for transportationhas limited applications because of battery storage rangeissues, although many recent successful demonstrations ofelectrical vehicles have been achieved. Renewable biofuelssuch as biodiesel and bioethanol are only a small percentageof the overall energy sources for mobility. With productionof oil predicted to decline, the number of transportationvehicles continuing to increase globally, and the realizationthat we live in a carbon constrained world, a transformationof the transportation sector is inevitable. Next generationsof transportation vehicles will not rely exclusively on theuse of fossil fuels burnt in an internal combustion engine.Furthermore, the hydrogen fuel cell proposition is not asattractive as first believed, as no gain is possible when thehydrogen is derived from electricity or fossil fuels. It istherefore important to have the ability to simulate differenttransportation vehicles configurations to enable the optimiza-tion of available renewable energy resources and minimizegreenhouse gases. Our ability to predict and investigatevarious transportation pathways can contribute effectively todecrease our reliance on fossil fuels.Hybrid vehicles offer the promise of higher energy efficiencyand reduced emissions when compared with conventional

automobiles, but they can also be designed to overcome therange limitations inherent in a purely electric automobilebyutilizing two distinct energy sources for propulsion. Withhybrid vehicles, energy is stored as a petroleum fuel and inan electrical storage device, such as a battery pack, and isconverted to mechanical energy by an internal combustionengine (ICE) and electric motor, respectively. The electricmotor is used to improve energy efficiency and vehicleemissions while the ICE provides extended range capability.Though many different arrangements of power sources andconverters are possible in a hybrid power plant, the twogenerally accepted classifications are series and parallel.Nowadays, researchers focus on understanding the dynamicsof the hybrid vehicles by developing the simulators [1]. Theresults can be used to optimize the design cycle of hybridvehicles by testing configurations and energy managementstrategies before prototype construction begins. Power flowmanagement, optimization of the fuel economy and reducingthe emissions using intelligent control systems are partof the current research [2]–[5]. Practical and experimentalverification of the vehicle simulators is an important part ofongoing researches [6].Several computer programs have since been developed todescribe the operation of hybrid electric power trains [7],in-cluding: simple EV simulation (SIMPLEV) from the DOE’sIdaho National Laboratory, MARVEL from Argonne Na-tional Laboratory, CarSim from AeroVironment Inc., JANUSfrom Durham University, ADVISOR from the DOE’s Na-tional Renewable Energy Laboratory [8] and Vehicle MissionSimulator [9]. Some of the works conducted by the hybridvehicle design team at Texas A&M University is reported inpapers by Ehsani et al. [10]–[12].The University of Manitoba, Canada, in cooperation withDemocritus University of Thrace, Greece, are developing aRenewable Energy Vehicle Simulator (REVS) that enablesto simulate renewable energy vehicles using combination ofpropulsion system and fuels by adapting library modules tosuit particular applications. The simulation software predictsthe energy use of the vehicle, taking into account the dutycycle and driver habits. Library modules have been developedto simulate the PHEV architecture as this platform offersenergy scenario for cars and buses allowing combinationof energy sources that include renewable electricity andrenewable biofuels. REVS was developed to address the next

Fig. 1. General schematic of the series parallel HEV.

generations of vehicles which will not rely exclusively on theuse of fossil fuels burnt in an internal combustion engine.The modeling of the internal combustion engines is carriedout by IDEAS. The modeling of the transmission system,dynamics of the vehicle, electrical motor and power driversis done using Matlab and Simulink. REVS simulates differentvehicles configurations to enable the optimization of avail-able renewable energy resources and minimize greenhousegases. This paper discusses the methodology for designingsystem level vehicles using the REVS package. A seriesparallel HEV with a conventional ICE and power splittransmission system have been designed using the simulationpackage. A fuzzy controller has been developed to simulatethe driver and to command the acceleration and brake pedal.Another fuzzy controller has been employed to manage thepower flow in hybrid vehicle. The simulation results arepresented for the vehicle.

II. REVS: MODULES AND DESIGN METHODOLOGY

REVS has been developed in Matlab/Simulink environ-ment as well as IDEAS [13], [14]. Mainly, the modules andcomponents related to drive trains, dynamics modeling andcontrol are developed in Simulink and the fluid and heattransfer systems are carried out by IDEAS. The commu-nication module transfers the data between Simulink andIDEAS at each time step. A user can select the componentsof the vehicle from the libraries and create a specific vehicleconfiguration. The vehicle can be constructed graphically byconnecting the main component blocks (environment, drivecycle, controller, engine, motor/generator, transmission, bat-teries, vehicle dynamics and renewable energy resources) us-

ing the Simulink visual programming methodology throughthe connection of the appropriate input and output ports. Onthe other hand, user can set the heat/fluid system components(engine chemical reactions, fuel, solar, fuel cells) usingtheIDEAS. Energy flow and electrical signals are the mainelements transferred between library modules. REVS imple-ments three kinds of controls: direct control, vehicle systemlevel control and component level control. Direct controlgoverns the flow of information from block to block in themodel. One block can control another block through outputconnectors; the same block can be controlled by anotherblock through input connectors. Signal and energy flow fromblock to block in the model create a direct control network.Results such as engine, motor and vehicle speeds, torque,power and emissions are displayed using the graphicalplotting tools that can consider transient responses. Vehiclecharacteristics such as size and weight, gear ratios, drag andfriction coefficients, inertias and the environmental situationscan be changed in an excel worksheet file to specify thedrive train. A controller block is designed with conventionaland fuzzy logic controller blocks which create the signalsrequired to control the individual system-level components.REVS has been designed to be flexible in adding on ofmore Matlab/Simulink toolboxes for optimization purposesand virtual reality interfaces.

III. D ESIGN OF THESERIESPARALLEL HYBRID

ELECTRIC VEHICLE

In this section, the design and analysis of the model ofToyota Prius as a series parallel HEV drive train usingthe REVS is discussed. The Prius’ components such as

Fig. 2. Model of series parallel HEV in REVS.

ICE, motor, battery, and vehicle dynamics models weredefined based on vehicle’s available information. The modelof the Power Split Device (PSD) and battery is explainedin paper by Liu et al. [15]. A description is given of theperformance specifications, the control strategies and powerplant developed for the vehicle design. A fuzzy controlleris designed to manage the output power of the electricmotor based on accelerating pedal and State of the Charge(SOC) of the battery. Another fuzzy controller parallel witha first order system has been employed to model the driverresponse to the vehicle velocity error. Simulation studiesareperformed for Prius using two different vehicle velocity drivecycle. Various performance parameters of the vehicle, suchas vehicle velocity, SOC and generated power by ICE andEM, during the simulation studies are graphically presentedin this paper.In a typical series parallel drive train design, consistingof anICE, an electric motor, a generator and a power split device

(PSD), either the ICE or the electric motor can be consideredthe primary energy source depending on the vehicle designand energy management strategy. The PSD divides the outputtorque of the ICE, with a fixed torque ratio, into the wheelsand generator. The output power of the ICE can be dividedinto an infinite ratio between the wheels and generator. Thisconfiguration is designed so that the ICE and electric motorare both responsible for propulsion or each is the primemover at a certain time in the drive cycle. Also part ofthe power of the ICE transfers to the wheels while theother is used to recharge an energy accumulator, usually abattery pack. The general schematic of the series parallelconfiguration in REVS is shown in Figure 1.Series parallel HEV consists of different elements withvarious configurations that make the vehicle modeling morecomplex by providing different number of choices and theireffect on a vehicles performance for a special mission.The modeling of Prius drive train is shown in details

in Fig. 2. The ICE model was designed based on Priustorque/power/velocity data and threshold using lookup table.The permanent magnet asynchronous AC motor of the Priusmodel is also modeled based on available data using lookuptable by considering the motor power threshold. Capacityand number of cells of the battery assumed as an initialinput parameter in the simulation. State of the charge ofthe battery and current load determine the DC bus voltagebased on battery model [15]. Regenerative braking is inher-ently performed through PSD and generator whenever thedecreasing velocity of the vehicle is demanded by driver. Afuzzy controller manipulates the power contributions of theelectric motor that is explained in detain in the followingsection.

Fig. 3. Schematic of the fuzzy logic power controller with membershipfunctions.

Fig. 4. 3D graph of the fuzzy controller rules for power controller; SOCand Acceleration pedal are inputs and Scaling factor is output.

IV. ENERGY MANAGEMENT

The energy management strategy to control the power flowof the vehicle is described in this section. Following criteria

should be considered in developing the energy managementblock: 1- The driver inputs (from brake and acceleratingpedals) is consistent with conventional vehicle (driving theseries parallel HEV should not ”feel” different from drivinga conventional vehicle). 2- The state of charge of the batteryis sufficient at all times. The power controller determines thepower needed to drive the wheels and charge the batteries. Italso commands the power required from electric motor. Thebatteries can be charged at the same time of power assignedto the electric motor. The ICE can provide the power forboth charging the batteries and driving the wheels usingPSD. The next section discusses the power controller thatimplements the energy management strategy and uses fuzzylogic to compute the power flow.

A. Power Controller

Figure 2 presents a simplified block diagram of the ve-hicle model. As shown in Fig. 2, a fuzzy logic controllerdetermines the output power of the EM with regard to theinputs of accelerator pedal and the SOC of the battery. Theacceleration pedal signal is normalized to a value betweenzero and one (zero: pedal is not pressed, one: pedal fullypressed). The normalized braking pedal signal is directlyconnected to the vehicle dynamic block to subtract brakingforces from wheel forces. The output of the power controllerblock is the scaling factor of the EM power which is normal-ized between zero and one. The scaling factor is multipliedby the maximum available power of the EM in EM block.On the other hand, the normalized value of the accelerationpedal multiplies by maximum available power of the ICE.Finally, the total power of the vehicle is the ICE+EM power.By this way, the driver can command the complete range ofavailable power at all times. The maximum available EMand ICE power depends on their speed and temperature,and is computed using a 2D look-up table with speed andtemperature as inputs of the EM and ICE blocks.

Fig. 5. Schematic of the fuzzy logic used to model the driver withmembership functions.

Fig. 6. Graph of the driver fuzzy controller rules; Velocityerror is outputand Acceleration pedal is input.

The EM scaling factor computed through fuzzy logic con-troller is close to zero when the SOC of the battery is too low.In that case the EM is not used to drive the wheels, in order toprevent battery damage. When the SOC is high enough, thescaling factor equals one. User can change the membershipfunctions of the input and output signals. With respect tothe SOC limitations, the scaling factor is proportional to theacceleration pedal. The membership functions of the powercontrol unit are illustrated in Fig. 3. To illustrate the fuzzylogic rules, Fig. 4 shows the scaling factor as a function ofacceleration pedal and SOC. As it is shown in Fig. 4, thescaling factor is zero when the SOC is below 0.8.

B. Velocity Tracking Controller

A combination of a low pass filter and a fuzzy controller isassumed to model the driver for tracking the desired velocity.The vehicle velocity error is assumed as the input of the lowpass filter of the form 0.1

0.05S+1. On the other hand, a fuzzy

controller, parallel with the first order system, commandsthe acceleration pedal. The membership functions of thedriver fuzzy controller have been shown in Fig. 5. When thevelocity of the vehicle is lower than desired one, the driverfuzzy controller sets a positive value for acceleration pedaland the more velocity error the more acceleration pedal. Thefunctionality of the rules of the driver fuzzy controller hasbeen shown in Fig. 6.

V. SIMULATION RESULTS

The vehicle has been simulated with REVS by velocitycommands. The parameters of the vehicle are listed in Table1. Two different sets of the desired vehicle velocity areapplied on the model to examine the response of the system.The first case examines the vehicle on acceleration, cruiseand deceleration and the results are shown in Fig. 7. Thesecond case is a random desired vehicle velocity that in-cludes the sharp accelerations and decelerations. By specifiedparameters of the system, the desired vehicle velocity shownby solid line, actual vehicle velocity shown by dashed line,SOC, scaling factor, acceleration pedal, electric motor power

0 20 40 60 80 1000

100

200

Veh

icle

spe

ed [k

ph]

0 20 40 60 80 100

0.70.80.9

SO

C

0 20 40 60 80 1000

0.2

0.4

Sca

ling

fact

or

0 20 40 60 80 1000

0.5

1

Acc

eler

atio

n pe

dal

0 20 40 60 80 1000

10

20

EM

Pow

er

0 20 40 60 80 1000

50

ICE

pow

er

0 20 40 60 80 1000

10

20

Gen

erat

or p

ower

Time [sec]

Fig. 7. Results of the simulation for an acceleration, cruiseand decelerationcase.

in KW, ICE power in KW and generator power in KW areshown in Fig. 7 and 8 for first and second cases respectively.As shown in Fig. 7 for the first case, the vehicle canreasonably track the desired velocity. It has also shown thatthe SOC is kept higher than 0.8. In addition, ICE providesthe main power to the vehicle during the cruise period that ishighly desirable. It can be seen that the SOC increases duringdeceleration period that is also one of the important factorsin hybrid electric vehicle designs to regenerate the power.The results of the second case in Fig. 8 show that the batterywas operated at a relatively high SOC (between 0.75 and themaximum 0.96) for the whole period of driving cycle. It alsodemonstrates that the controller is well defined to minimizethe velocity error. The time period of 10 sec to 30 secshows the inherent limitation of the vehicle available powerthat causes large vehicle velocity error. For proposed timeperiod, the power fuzzy controller commands the maximumacceleration pedal and respectively the maximum availableEM power has been used by considering the limitation ofthe SOC. Fig. 8 shows that by reducing the SOC, ICE is themajor source of energy for acceleration of the vehicle.

0 50 100 150 200 250 300 3500

50

100

Veh

icle

spe

ed [k

ph]

0 50 100 150 200 250 300 350

0.70.80.9

SO

C

0 50 100 150 200 250 300 3500

0.5

1

Sca

ling

fact

or

0 50 100 150 200 250 300 3500

0.5

1

Acc

eler

atio

n pe

dal

0 50 100 150 200 250 300 3500

10

20

EM

Pow

er

0 50 100 150 200 250 300 3500

50

ICE

pow

er

0 50 100 150 200 250 300 3500

10

20

Gen

erat

or p

ower

Time [sec]

Fig. 8. Results of the simulation for a desired vehicle velocity.

Definition ValuesCurb weight 1600Kg

Battery Capacity 10 KWh

Max ICE power 57 kW 5,000 r.p.m.MAX ICE torque 115 Nm 4,200 r.p.m

EM power 50 kW 1200 - 1540 r.p.m.Maximum voltage 500 V

Maximum EM torque 400 N.m 0 - 1200 r.p.m.

TABLE I

PARAMETERS USED IN PAPER.

VI. CONCLUSIONS

A renewable energy vehicle simulator REVS, for mod-eling, simulation, and analysis of a drive train developedat University of Manitoba using Matlab/Simulink/IDEAShas been presented in this paper. The goal of REVS is tostudy issues related to plug in hybrid electric vehicle designsuch as dynamics, energy management, fuel economy. REVSprovides new libraries to model different vehicle configu-rations in addition to Matlab/Simulink standard toolboxes.The design of a series parallel electric vehicle, Toyota Prius,is presented. Fuzzy controller has been used to control thepower flow as well as to track the vehicle velocity. Theresults of velocity tracking performance, SOC, accelerationpedal commanded by fuzzy controller and electric motorcommand are illustrated to show the flexibility of the REVSfor studying various issues related to electric and hybrid EVdesign.

VII. A CKNOWLEDGMENT

This project was supported by Mathematics of InformationTechnology and Complex systems (MITACS) and ManitobaHydro through the MITACS internship program.

REFERENCES

[1] S. R. Cikanek, K. E. Bailey and B. K. Powell,Parallel Hybrid ElectricVehicle Dynamic Model and Powertrain Control.Proceedings of theAmerican Control Conference, Albuquerque, New Mexico, June1997.

[2] L. Ippoliot, V. Loia and P. Siano,Extended Fuzzy C-Means andGenetic Algorithms to Optimize Power Flow Management in HybridElectric Vehicles.Fuzzy Optimization and Decision Making, 2, 359374, 2003.

[3] P. Bowles, H. Peng, X. Zhang,Energy Management in a ParallelHybrid Electric Vehicle With a Continuously Variable Transmission.American Control Conference Chicago, Illinois, June 2000.

[4] Jong-Seob Won, R. Langari and M. Ehsan,An Energy Managementand Charge Sustaining Strategy for a Parallel Hybrid Vehicle WithCVT. IEEE Transactions on control system technology, VOL. 13, NO.2, MARCH 2005.

[5] D. Hermance and S. Sasaki,Hybrid electric vehicles take the streets.IEEE Spectrum, pp. 4852, Nov. 1998.

[6] C. Bourne, P. Faithfull and C. Quigley,Implementing Control of aParallel Hybrid Vehicle.International Journal of Vehicle Design, 1996.17(5-6): p. 649-62.

[7] Karen L. Bulter, M. Ehsani and P. Kamath,A Matlab–based modelingand simulation package for electric and hybrid electric vehicle design.IEEE Transaction of Vehicular Technology, Vol 48. NO. 6. November1999.

[8] K. B. Wipke and M. R. Cuddy,Using an advanced vehicle simulator(ADVISOR) to guide hybrid vehicle propulsion system development.available at: http://www.hev.doe.gov.

[9] R. Noons, J. Swann, and A. Green,The use of simulation software toassess advanced power trains and new technology vehicles.in Proc.Electric Vehicle Symp. 15, Brussels, Belgium, Oct. 1998.

[10] M. Ehsani, K. M. Rahman, and H. A. Toliyat,Propulsion systemdesign of electric and hybrid vehicles.IEEE Trans. Ind. Electron.,vol. 44, pp. 1927, Feb. 1997.

[11] H. A. Toliyat, K. M. Rahman, and M. Ehsani,Electric machine inelectric and hybrid vehicle application.in Proc. ICPE95, Seoul, pp.627635.

[12] M. Ehsani,Introduction to ELPH: A parallel hybrid vehicle concept.in Proc. ELPH Conf., College Station, TX, Oct. 1994, pp. 1738.

[13] M. McGarry and M. Watson,Dynamic simulation: a maturing technol-ogy generating real benefits.Ideas Simulation, available at www.ideas-simulation.com.

[14] G. A. Pugh, , 2001.,Extending Simulation.’ Indiana University-PurdueUniversity.

[15] J. Liu, H. Peng and Z. Filipi,Modeling and Control Analysis of ToyotaHybrid System.International Conference on Advanced IntelligentMechatronics. 2005 IEEE/ASME pp. 134- 139.