vehicle stability enhancement of four-wheel-drive hybrid electric vehicle using rear motor control

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 2, MARCH 2008 727

Vehicle Stability Enhancement of Four-Wheel-DriveHybrid Electric Vehicle Using Rear Motor Control

Donghyun Kim, Sungho Hwang, and Hyunsoo Kim

Abstract—A vehicle stability enhancement control algorithmfor a four-wheel-drive hybrid electric vehicle (HEV) is proposedusing rear motor driving, regenerative braking control, and elec-trohydraulic brake (EHB) control. A fuzzy-rule-based controlalgorithm is proposed, which generates the direct yaw momentto compensate for the errors of the sideslip angle and yaw rate.Performance of the vehicle stability control algorithm is evalu-ated using ADAMS and MATLAB Simulink cosimulations. HEVchassis elements such as the tires, suspension system, and steeringsystem are modeled to describe the vehicle’s dynamic behavior inmore detail using ADAMS, whereas HEV power train elementssuch as the engine, motor, battery, and transmission are modeledusing MATLAB Simulink with the control algorithm. It is foundfrom the simulation results that the driving and regenerativebraking at the rear motor is able to provide improved stability.In addition, better performance can be achieved by applying thedriving and regenerative braking control, as well as EHB control.

Index Terms—Four-wheel-drive (4WD), hybrid electric vehicle(HEV), regenerative braking, vehicle stability control.

NOTATION

i CVT speed ratio.T Torque (in newton meters).ω Rotational speed (in revolutions per minute).J Moment of inertia (in kilogram square meters).Q Battery capacity (in ampere hour).C Tire cornering stiffness (in newtons per radian).CG Center of gravity.F Force (in newtons).I Moment of inertia (in kilogram square meters).L Wheel base (in meters).Llook Look-ahead distance (in meters).M Moment (in newton meters).N Static normal load (in newtons).V Vehicle velocity (in kilometers per hour).g Gravitational acceleration (in meters per second

squared).h Height of CG (in meters).m Vehicle mass (in kilograms).w Vehicle tread (in meters).x∗ Estimated longitudinal displacement (in meters).y∗ Estimated lateral displacement (in meters).

Manuscript received October 29, 2005; revised July 17, 2006, July 5, 2007,and July 6, 2007. The review of this paper was coordinated by Dr. M. AbulMasrur.

The authors are with the School of Mechanical Engineering, SungkyunkwanUniversity, Suwon 440-746, Korea (e-mail: [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2007.907016

α Tire slip angle (in radians).β Vehicle body sideslip angle (in radians).γ Vehicle yaw rate (in radians per second).δ Steering angle (in radians).ψ Vehicle heading angle (in radians).µ Tire–road friction coefficient.

Subscript

f Front.r Rear.

I. INTRODUCTION

HYBRIDIZATION of the four-wheel-drive (4WD) vehicleby adopting separate motors at the front and rear wheels

provides many advantages. First, an additional mechanical de-vice, such as a transfer case and propeller shaft that are requiredto transfer the engine power to the wheels, can be eliminatedby adopting separate motors at the front and the rear wheels.Second, an improvement in fuel economy can be achievedby recapturing energy from the regenerative braking. Finally,improved vehicle stability can be obtained with adequate con-trol of the motor drive torque and the regenerative brakingtorque [1].

Generally, vehicle stability in 4WD vehicles has beenpursued by torque split-based and brake-based technologies.Brake-based methods are essentially brake-maneuver strate-gies that use the active control of the individual wheel brake.By comparison, torque-based technologies realize stability byvarying traction torque split through the power train to createan offset yaw moment [2].

Recently, vehicle safety enhancement systems, known aselectronic stability program or vehicle dynamic control, thatadopt the brake-based methods have become very popular,and applications of these systems have expanded. When acar encounters unexpected road conditions, such as a split-µroad, the tire slip angles and, consequently, the vehicle slipangle may rapidly increase, which causes the car to reach itsphysical limit of adhesion between the tires and the road. Sincemost drivers have less experience operating a car under thissituation, they might eventually lose control of the vehicle. Thebrake-based vehicle safety enhancement system controls thepredictability of vehicle behavior by using the active controlof the individual wheel brake so that the driver can reestablishcontrol of the vehicle. As brake-based technologies, vehiclesafety enhancement systems, such as the offset yaw momentgeneration using the brake force control of the each wheel

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728 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 57, NO. 2, MARCH 2008

[3] and the wheel slip control based on the estimated frictioncoefficient between the tire and the road [4]–[6], have beeninvestigated. Although brake-based technology has been provento be effective in providing vehicle safety, it does have thedrawback of causing the vehicle speed to slow down too muchagainst the driver’s demand. On the other hand, vehicle safetyis pursued by controlling the drive torque by using a torquesplit device, such as viscous coupling [7] and electromagneticcoupling [8], as the torque-based technology. However, the lim-itation of the torque-based method is that it cannot accuratelycontrol the individual wheel torque. Therefore, a vehicle safetyenhancement system that fulfills both the safety requirement, aswell as the driver’s demand, is required.

In the 4WD HEV that adopts separate front and rear motors,the vehicle stability enhancement algorithm using the motorcontrol has some advantages, such as faster response, brakingenergy recapturing, etc. [9]. However, since the left and rightwheels are controlled by the same driving and regenerativetorque from one motor, stability enhancement only by the rearmotor control has a limitation in satisfying the required offsetyaw moment. Therefore, to obtain the demanded offset yawmoment, a brake force distribution at each wheel is required.

In this paper, a vehicle stability control logic using the rearmotor and electrohydraulic brake (EHB) is proposed for a 4WDHEV. A fuzzy control algorithm is suggested to compensate forthe error of the sideslip angle and the yaw rate by generating thedirect yaw moment. Performance of the vehicle stability controlalgorithm is evaluated using ADAMS and MATLAB Simulinkcosimulations.

II. VEHICLE MODELING

A. MATLAB Simulink Power Train Model

Fig. 1 shows the 4WD HEV power train structure investi-gated in this study. Dynamic models of the 4WD HEV powertrain, such as the engine, motor, battery, clutch, continuouslyvariable transmission (CVT), and controller, are obtained usingMATLAB Simulink on a modular base.1) Engine: The state equation of the engine is expressed as

Je ·dωe

dt= Te − Tloss − Tnet (1)

where Je is the engine inertia, ωe is the engine speed, Tloss

is the auxiliary device loss, and Tnet is the CVT input torque.The engine torque dynamics is modeled by the first-ordersystem as

Te

Te_desire=

11 + τes

(2)

where Te_desire is the desired engine torque, and τe is the enginetorque time constant.2) Motor: The front and rear motor torque is determined

as the smaller torque by comparing the target motor torque,which is calculated from the controller, and the maximummotor torque available at the present motor speed. Using themotor torque and speed, the motor efficiency is determinedfrom the efficiency map. Once the required battery power to

Fig. 1. Four-wheel-drive HEV power train structure.

drive the motor is obtained, the voltage and current of thebattery are obtained from the battery model. Since the motortorque dynamics is very fast compared with other power trainelements’ dynamics, the motor torque dynamics is modeled bya first-order system as

Tm

Tm_desire=

11 + τms

(3)

where Tm_desire is the desired motor torque, and τm is themotor torque time constant.3) Battery: In this paper, the input and output currents of

the battery and the state of charge (SOC) are calculated usingthe battery internal resistance model. The internal resistancesare obtained from the experiments with respect to the batterySOCs. The battery voltage is represented as

Ua =E − IRi at discharge (4)

Ua =E + IRi at charge (5)

where Ua is the voltage, E is the electromotive force, I is thecurrent, and Ri is the internal resistance. The battery’s SOC isdirectly related to the battery capacity, which is defined as

Qu(I, t, κ) = Qτ (κ, I) −t∫

0

I(t)dt (6)

where Qu is the temporary usable capacity, which is a functionof the current I , temperature κ, and time t. Qτ is the battery’scapacity. The integral term in (6) is the usable charge that hasbeen drawn from the battery.4) CVT: The CVT ratio needs to be controlled, depending

on the operation mode. In the 4WD HEV, there are two modesthat are defined: 1) hybrid electric vehicle (HEV) mode and2) zero emission vehicle (ZEV) mode. In the HEV mode,where the vehicle is driven by the engine and the motors, thedesired CVT ratio is controlled to move the engine operationpoint on the optimal operation line (OOL) for minimum fuel

KIM et al.: VEHICLE STABILITY ENHANCEMENT OF FOUR-WHEEL-DRIVE HYBRID ELECTRIC VEHICLE USING REAR MOTOR CONTROL 729

Fig. 2. MATLAB Simulink power train model for a 4WD HEV.

consumption. Therefore, the desired CVT speed ratio id for theminimum fuel consumption is defined as

id =Rt · ωd

Nd · V (7)

where Rt is the tire radius, Nd is the final reduction gear ratio,ωd is the desired engine speed that can be obtained as a pointwhere the OOL and the throttle valve opening curve cross eachother. In the ZEV mode, where the vehicle is propelled onlyby the motors, the desired CVT ratio needs to be controlled tooperate the front motor at the best efficiency region. The desiredCVT speed ratio id in the ZEV mode is defined as

id =Rt · ωmf

Nd · V (8)

where V is the present vehicle velocity, and wmf is the frontmotor speed. Since the rear motor is not connected with theCVT, it is controlled to generate the required power.

The CVT speed ratio shift dynamics is modeled by theexperimental equation as [10]

di

dt= σ(i) |ωp|

(Pp − P ∗

p

)(9)

where σ(i) is the coefficient that is a function of the speed ratioi, ωp is the primary actuator speed, Pp is the primary actuatorpressure, and P ∗

p is the primary actuator pressure at steady state.Fig. 2 shows the MATLAB Simulink power train model for

a 4WD HEV investigated in this study.

B. ADAMS Vehicle Model

In high-speed cornering or emergency braking, the tire slipand lateral force that determine the vehicle’s dynamic behaviorare greatly affected by the tire nonlinear characteristic, thesteering system, and the suspension system. Therefore, a vehi-

Fig. 3. ADAMS full-car model.

cle model that is able to describe the dynamic characteristicsof these systems is required. In addition, in order to representthe independent driving characteristics of the front and rearwheels of the 4WD HEV, a detail vehicle model is required.In this paper, a vehicle model using ADAMS is developed byconsidering the actual vehicle chassis design parameters.

Fig. 3 shows the 4WD HEV model using the ADAMS pro-gram [11]. In the ADAMS vehicle model, longitudinal velocity,lateral velocity, yaw rate, roll angle, pitch angle, sideslip angle,and longitudinal and lateral displacements are calculated. TheADAMS vehicle model in Fig. 3 provides the reliable dynamicbehavior of the vehicle since the dynamic characteristics ofthe chassis components such as the tires, the steering system,and the front and rear suspension systems can accurately bedescribed from multibody dynamic analysis by the ADAMSsolver. As shown in Fig. 3, every chassis component is modeled


Fig. 4. Cosimulation structure for ADAMS and MATLAB Simulink.

Fig. 5. Vehicle motion and parameters.

as a rigid body and is connected with joint and bush to simulatethe vehicle’s dynamic motion.

In Fig. 4, the cosimulation structure is shown. In theADAMS/MATLAB cosimulation, the front and rear drive axletorque and the friction brake torque that are calculated fromthe MATLAB Simulink model are transmitted to the ADAMSmodel, while the vehicle velocity, sideslip angle, yaw rate,wheel slip angle, etc., are transferred from the ADAMS modelto the MATLAB Simulink model.

III. VEHICLE STABILITY CONTROL

When a vehicle travels around a sharp corner or a driverexcessively maneuvers the steering wheel, the rear-tire slipangle may exceed its limit value, which results in reduced rearlateral forces. This causes the vehicle sideslip angle and the yawrate to increase. The grip is lost, and consequently, steerabilitybecomes out of control. Therefore, to ensure vehicle stability,an appropriate vehicle safety enhancement system should beprovided to assist the driver in recovering control of the vehicle.In this paper, a control algorithm using the regenerative brakingwith EHB is proposed.

Fig. 6. Driver model.

A. Equation of Vehicle Motion

Vehicle motion in longitudinal, lateral, and yaw directions(Fig. 5) can be expressed as follows:

mV̇ =∑

Fx = Fxfr + Fxfl + Fxrr + Fxrl (10)

mV (β̇ + γ) =∑

Fy = Fyfr + Fyfl + Fyrr + Fyrl (11)

Iz γ̇ =∑

Mz = (Fxfr + Fxfl) · Lf

− (Fxrr + Fxrl)Lr + M (12)

M = −w

2(Fxfr − Fxfl + Fxrr − Fxrl) (13)

where F is the tire force, Iz is the moment of inertia, L is thewheel base, M is the direct yaw moment that is generated fromthe tire force at each wheel, V is the vehicle velocity, β is thesideslip angle, γ is the yaw rate, m is the vehicle mass, w isthe vehicle tread, x is the longitudinal direction, y is the lateraldirection, z is the vertical direction, fr is the front right wheel,fl is the front left wheel, rr is the rear right wheel, and rl is therear left wheel.

In vehicle stability control, the direct yaw moment M is usedas the control input of the system, while the tire force F at eachwheel, sideslip angle β, and yaw rate γ are calculated from theADAMS model.


Fig. 7. Block diagram of vehicle stability control.

Fig. 8. Flowchart of vehicle stability control.

B. Driver Model

A driver model is used to trace the desired path for theclosed-loop simulation. Fig. 6 shows a schematic diagram ofthe steering driver model.

The steering driver model manipulates the steering angleto compensate the error between the estimated position andthe desired position. The estimated position x∗ and y∗ can becalculated from the following equations [12]:

x∗ =x + (Vx cos ψ − Vy sin ψ) · Llook

V(14)

Fig. 9. Membership function for the fuzzy controller.

TABLE IRULE BASE FOR THE FUZZY CONTROLLER

y∗ = y + (Vx sin ψ + Vy cos ψ) · Llook

V(15)

e =√

(xd − x∗)2 + (yd − y∗)2 (16)

δ = PID(s) · e · exp(−τδs) (17)

where x∗ is the estimated longitudinal displacement, y∗ is theestimated lateral displacement, xd is the desired longitudinal


Fig. 10. Yaw moment generation. (a) Oversteer control. (b) Understeer control. (c) Flow chart of yaw moment control.

displacement, yd is the desired lateral displacement, δ is thesteering angle, ψ is the vehicle heading angle, e is the errorof the displacement between the estimated position and thedesired position, Llook is the look ahead distance, PID(s)is the PID control gain, and τδ is the human-response-timeconstant for steering. Equation (17) is proposed to describethe driver’s response, which manipulates the steering angle δthat corresponds to the position error e. In (17), τδ = 0.3 isused by considering the average human response delay time forperception [13].

C. Desired Vehicle Model

The error e that is obtained from (16) is transformed intothe steering angle δ by considering the control gain PID and

the steering response delay in (17) and (18). From the steeringangle δ, the desired yaw rate γd and the desired sideslip angleβd can be obtained as follows [12]:

γd =1

1 + As · V 2· V

L· δ (18)

βd =1 − m

2L · Lf

LrCrV 2

1 + As · V 2· Lr

L· δ (19)

As =m

2L2· LrCr − LfCf

Cf · Cr(20)

where γd is the desired yaw rate, βd is the desired sideslip angle,As is the steering stability factor, Cf is the front-tire corneringstiffness, and Cr is the rear-tire cornering stiffness.


D. Fuzzy Control Algorithm

For the vehicle stability control, a fuzzy control algorithmis used by considering the tire nonlinear characteristics incornering [14]–[16]. The inputs of the fuzzy controller arethe errors of the vehicle sideslip angle and the yaw rate. Theerror is defined as the difference between the desired valuefrom the desired vehicle model and the actual value from theactual vehicle model. Using these inputs, the fuzzy controllergenerates the direct yaw moment that is required to compensatethe errors.

In Figs. 7 and 8, a block diagram and a flowchart for the ve-hicle stability control are shown. For the desired displacementsxd and yd, the driver model manipulates the steering angle δ.The actual sideslip angle β and yaw rate γ are measured andcompared with the desired sideslip angle βd and yaw rate γd,which are calculated from the desired value estimator in (18)and (19). βerror and γerror are used as the inputs of the fuzzycontroller.

The fuzzy controller consists of a triangular membershipfunction (Fig. 9) that gives the direct yaw moment output for theinputs of the yaw rate and sideslip angle errors. The rule baseused in the fuzzy controller is shown in Table I. The rule baseconsists of the five linguistic variables—negative big (NB), neg-ative small (NS), zero (ZR), positive small (PS), and positivebig (PB)—and is arranged by the center-of-gravity method [17].The fuzzy controller calculates the direct yaw moment M tocompensate the errors. The direct yaw moment M is usedas the system control input. In generating the required directyaw moment M , the following control strategy is proposedto maximize the recapturing energy and fast response: M isgenerated by the rear motor driving and regenerative brakingcontrols, in priority, and if the direct yaw moment by the rearmotor control is not sufficient enough, M is compensated bythe EHB force at the front and rear wheels.

Fig. 10 shows how the yaw moment is generated by the rearmotor and EHB module with respect to the yaw rate error.When the yaw rate error eγ becomes negative, the vehicleshows oversteer characteristics, and vice versa. For the over-steer case [Fig. 10(a)], the rear motor is controlled to carry outthe regenerative braking to generate the direct yaw moment.When the regenerative braking is executed at the rear wheel,the longitudinal force applied at the tire decreases, which resultsin the decreased slip in the longitudinal direction. This causesincreased lateral force at the tire, according to the tire model.Since the lateral force on the front tire remains almost constant,the increased lateral force on the rear tire generates the yawmoment in the opposite direction, which operates to reduce thesideslip angle and the yaw rate.

If the direct yaw moment by the regenerative braking is notlarge enough to control β and γ, the EHB module begins tocome into action, together with the regenerative braking.

In the case of understeer [Fig. 10(b)], the rear motor iscontrolled to provide tractive force, which generates the directyaw moment to assist the vehicle cornering motion. When thetractive force is applied at the rear wheel, the lateral forceon the rear tire decreases. Since the lateral force on the fronttire remains unchanged, the decreased lateral force on the rear

TABLE IIVEHICLE SPECIFICATION

tire generates the yaw moment in the direction that reducesundersteer. Fig. 10(c) shows the flowchart of yaw momentcontrol for oversteer and understeer.

IV. SIMULATION RESULTS AND DISCUSSION

Four-wheel-drive HEV performance simulations are carriedout for a J-turn and a single-lane change. Table II lists thevehicle parameters used in the simulations.

A. J-Turn Simulation

Fig. 11 shows the simulation results for the J-turn [18]. Inthe simulation, the steering angle input is applied with 56◦,as shown in Fig. 11(a), at 80 km/h constant velocity underthe slippery road condition of µ = 0.2. In Fig. 11, simulationresults of (b) the yaw rate, (c) yaw rate error, (d) sideslipangle, and (e) vehicle trajectory are shown. In vehicle dynamiccontrol, the target yaw rate is calculated from the desired model.As shown in Fig. 11, the actual yaw rate without any control(No control) rapidly increases right after the steering input isapplied, which causes the vehicle to spin (e) in the counter-clockwise direction. In the case of the rear motor control (Motoronly), the sideslip angle, yaw rate, and vehicle trajectory followthe targets, showing some errors. The vehicle attitude showssome spin, but it is noted that the amount of spin is reduceda lot when compared to that of No control. From Fig. 11,it is found that vehicle stability can be improved only by the rearmotor control. To achieve better performance, the EHB mustbe applied at the right side of the wheels. Simulation resultsusing the EHB are shown in Fig. 11. In the simulation, therear motor control is applied with the EHB, and the brakingforce by the EHB module is applied only for the right-sidewheels to generate the required direct yaw moment. As shownin Fig. 11, the sideslip angle and yaw rate for the rear motorcontrol with EHB (Motor + EHB) follow the control targets,showing reduced errors when compared to those of the Motoronly. Correspondingly, the vehicle trajectory (e) follows thetarget trajectory closely, while the vehicle attitude is maintainedwithout spin. When the yaw rate error (c) becomes positive, themotor generates the tractive force needed to reduce understeer.


Fig. 11. Simulation results for a J-turn.

Fig. 12. Simulation results for a single-lane change.

When the yaw rate error becomes negative, the motor carriesout the regenerative braking to reduce oversteer. As shown inFig. 11(f), the dynamic behavior of the vehicle for each casecan be monitored by using the ADAMS animation tool.

B. Single-Lane Change Simulation

Fig. 12 shows the simulation results for a single-lane change.In the simulation, a sine-wave steering input (a) is applied at80 km/h constant velocity under the slippery road conditionof µ = 0.2. Fig. 12 shows (b) the yaw rate, (c) the yaw rateerror, (d) the sideslip angle, and (e) the vehicle trajectory. Thesideslip angle and yaw rate for No control come out of the target

value. The sideslip angle and yaw rate for Motor only showimproved response, but they still have some errors in followingthe target value. It is noted that the vehicle stability controlwith Motor + EHB follows the target value most closely. Asshown in Fig. 12(c), the yaw rate error shows either a positiveor negative value, which means that the vehicle experiences theundersteer or oversteer motion. Corresponding to the yaw rateerror, the rear motor generates the tractive force or regenera-tive braking force, respectively. The dynamic behavior of thevehicle for a single-lane change can be monitored by using theADAMS animation tool, as shown in Fig. 12(f).

From Figs. 11 and 12, it is found that the vehicle stabilitycontrol logic suggested in this paper demonstrates a satisfactory


performance. Compared to the EHB-only braking, the vehiclestability enhancement algorithm using the regenerative brakingplus EHB is able to provide improved vehicle stability andadditional improvement in fuel economy due to regenerativebraking.

V. CONCLUSION

Vehicle stability control for a 4WD HEV has been investi-gated using rear motor and EHB controls. A fuzzy-rule-basedcontrol algorithm was proposed, which generates the direct yawmoment to compensate for the errors of the sideslip angle andthe yaw rate between the outputs of the desired value estimatorand the actual vehicle model. Performance of the vehicle stabil-ity control algorithm is evaluated using ADAMS and MATLABSimulink cosimulations. The ADAMS model calculates theactual vehicle behavior such as the yaw rate, sideslip angle,lateral acceleration, and vehicle velocity by considering the tirenonlinearity, suspension characteristics, and steering system.The MATLAB Simulink model calculates the axle torque bythe rear motor and the EHB force at each wheel from the powertrain model and the control logic. It is found from the simulationresults that the direct yaw moment generated by the rear motorcontrol is able to provide improved stability compared with thevehicle performance without any control. In addition, betterperformance can be achieved by applying the rear motor plusthe EHB control. It is expected that the vehicle stability controlalgorithm suggested in this paper is able to offer an additionalimprovement in fuel economy, owing to the regenerative brak-ing energy, as well as improved vehicle stability.

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ISO Std. 7401.

Donghyun Kim received the B.S., M.S., andPh.D. degrees in mechanical engineering fromSungkyunkwan University, Suwon, Korea, in 2001,2003, and 2007, respectively.

He currently works as a Postdoctorate Fellowwith the School of Mechanical Engineering,Sungkyunkwan University. His main research inter-ests include vehicle stability enhancement control,optimal power distribution and regenerative brakingalgorithms for four-wheel-drive hybrid electricvehicles, fuel cell vehicles, and in-wheel electricvehicles.

Sungho Hwang received the B.S. degree in mechan-ical design and production engineering and the M.S.and Ph.D. degrees in mechanical engineering fromSeoul National University, Seoul, Korea, in 1988,1990, and 1997, respectively.

From 1992 to 2002, he was a Senior Researcherwith the Korea Institute of Industrial Technology,Seoul. He is currently an Associate Professor withSungkyunkwan University, Suwon, Korea, where hehas also worked with the School of MechanicalEngineering. His research interests are in the areas

of automotive mechatronics systems for fuel cell and hybrid electric vehiclesand embedded systems for x-by-wire systems.

Prof. Hwang is a member of the American Society of Mechanical Engineers,the American Society for Engineering Education, the Korean Society of Me-chanical Engineers, the Korean Society of Automotive Engineers, the Instituteof Control, Robotics, and Systems, and the Korean Fluid Power Systems(KFPS) Society. He has served as one of the directors of KFPS since 2005.

Hyunsoo Kim received the B.S. degree in mechan-ical engineering from Seoul National University,Seoul, Korea, in 1977, the M.S. degree in mechan-ical engineering from Korea Advanced Institute ofScience and Technology, Seoul, in 1979, and thePh.D. degree in mechanical engineering from theUniversity of Texas, Austin, in 1986.

From 2003 to 2005, he was a Chairman with theSchool of Mechanical Engineering. From 2005 to2007, he was the Head of the Center for InnovativeEngineering Education, Sungkyunkwan University,

Suwon, Korea, where he is currently a Professor and the Dean of the Collegeof Engineering. He is an Associate Editor for the International Journal of Au-tomotive Technology. He is the author of numerous journal articles and patents.His main research interests include hybrid electric vehicle (HEV) transmissionsystem design, regenerative braking and optimal power distribution algorithmsfor HEVs, vehicle stability control for HEVs, and in-wheel electric vehicles.

Prof. Kim is the Chair of the Hybrid and Fuel Cell Vehicle Division, KoreanSociety of Automotive Engineers. He received the Best Paper Award from theKorean Science and Technology Foundation in 2001 and the Baekam ExcellentPaper Award from the Korean Society of Mechanical Engineers in 1991.

vehicle stability enhancement of four-wheel-drive hybrid electric vehicle using rear motor control

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