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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 modeled

using 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) vehicle

by 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 required

to transfer the engine power to the wheels, can be eliminated

by adopting separate motors at the front and the rear wheels.

Second, an improvement in fuel economy can be achieved

by 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 been

pursued 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 by

varying traction torque split through the power train to create

an offset yaw moment [2].

Recently, vehicle safety enhancement systems, known as

electronic stability program or vehicle dynamic control, that

adopt 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-µ

road, the tire slip angles and, consequently, the vehicle slip

angle may rapidly increase, which causes the car to reach its

physical limit of adhesion between the tires and the road. Since

most drivers have less experience operating a car under this

situation, they might eventually lose control of the vehicle. The

brake-based vehicle safety enhancement system controls the

predictability of vehicle behavior by using the active control

of the individual wheel brake so that the driver can reestablish

control of the vehicle. As brake-based technologies, vehicle

safety enhancement systems, such as the offset yaw moment

generation 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 friction

coefficient between the tire and the road [4]–[6], have been

investigated. Although brake-based technology has been proven

to be effective in providing vehicle safety, it does have the

drawback of causing the vehicle speed to slow down too much

against the driver’s demand. On the other hand, vehicle safety

is pursued by controlling the drive torque by using a torquesplit device, such as viscous coupling [7] and electromagnetic

coupling [8], as the torque-based technology. However, the lim-

itation of the torque-based method is that it cannot accurately

control the individual wheel torque. Therefore, a vehicle safety

enhancement system that fulfills both the safety requirement, as

well 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 motor

control has some advantages, such as faster response, braking

energy recapturing, etc. [9]. However, since the left and right

wheels are controlled by the same driving and regenerative

torque from one motor, stability enhancement only by the rear

motor control has a limitation in satisfying the required offset

yaw moment. Therefore, to obtain the demanded offset yaw

moment, a brake force distribution at each wheel is required.

In this paper, a vehicle stability control logic using the rear

motor and electrohydraulic brake (EHB) is proposed for a 4WD

HEV. A fuzzy control algorithm is suggested to compensate for

the error of the sideslip angle and the yaw rate by generating the

direct yaw moment. Performance of the vehicle stability control

algorithm is evaluated using ADAMS and MATLAB Simulink

cosimulations.

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 power

train, such as the engine, motor, battery, clutch, continuously

variable transmission (CVT), and controller, are obtained using

MATLAB Simulink on a modular base.

1) Engine: The state equation of the engine is expressed as

J e ·dωe

dt= T e − T loss − T net (1)

where J e is the engine inertia, ωe is the engine speed, T loss

is the auxiliary device loss, and T net is the CVT input torque.The engine torque dynamics is modeled by the first-order

system as

T e

T e_desire=

1

1 + τ es(2)

whereT e_desire is the desired engine torque, and τ e is the engine

torque 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 maximum

motor torque available at the present motor speed. Using the

motor 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 the

battery are obtained from the battery model. Since the motor

torque dynamics is very fast compared with other power train

elements’ dynamics, the motor torque dynamics is modeled by

a first-order system as

T m

T m_desire=

1

1 + τ ms(3)

where T m_desire is the desired motor torque, and τ m is the

motor torque time constant.

3) Battery: In this paper, the input and output currents of

the battery and the state of charge (SOC) are calculated using

the battery internal resistance model. The internal resistances

are obtained from the experiments with respect to the battery

SOCs. The battery voltage is represented as

U a =E − IRi at discharge (4)

U a =E + IRi at charge (5)

where U a is the voltage, E is the electromotive force, I is the

current, and Ri is the internal resistance. The battery’s SOC is

directly 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 function

of the current I , temperature κ, and time t. Qτ is the battery’s

capacity. The integral term in (6) is the usable charge that has

been 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 modes

that are defined: 1) hybrid electric vehicle (HEV) mode and

2) zero emission vehicle (ZEV) mode. In the HEV mode,

where the vehicle is driven by the engine and the motors, the

desired CVT ratio is controlled to move the engine operationpoint on the optimal operation line (OOL) for minimum fuel

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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 the

minimum fuel consumption is defined as

id =Rt · ωdN d · V

(7)

where Rt is the tire radius, N d is the final reduction gear ratio,

ωd is the desired engine speed that can be obtained as a point

where the OOL and the throttle valve opening curve cross each

other. In the ZEV mode, where the vehicle is propelled only

by the motors, the desired CVT ratio needs to be controlled to

operate the front motor at the best efficiency region. The desired

CVT speed ratio id in the ZEV mode is defined as

id =Rt · ωmf

N d · V (8)

where V is the present vehicle velocity, and wmf is the front

motor speed. Since the rear motor is not connected with the

CVT, it is controlled to generate the required power.

The CVT speed ratio shift dynamics is modeled by the

experimental equation as [10]

didt

= σ(i) |ω p|P p − P ∗ p

(9)

where σ(i) is the coefficient that is a function of the speed ratio

i, ω p is the primary actuator speed, P p is the primary actuator

pressure, 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 slip

and lateral force that determine the vehicle’s dynamic behavior

are 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 characteristics

of these systems is required. In addition, in order to representthe independent driving characteristics of the front and rear

wheels of the 4WD HEV, a detail vehicle model is required.

In this paper, a vehicle model using ADAMS is developed by

considering 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. The

ADAMS vehicle model in Fig. 3 provides the reliable dynamic

behavior of the vehicle since the dynamic characteristics of

the chassis components such as the tires, the steering system,

and the front and rear suspension systems can accurately be

described from multibody dynamic analysis by the ADAMSsolver. As shown in Fig. 3, every chassis component is modeled

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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 simulate

the vehicle’s dynamic motion.

In Fig. 4, the cosimulation structure is shown. In the

ADAMS/MATLAB cosimulation, the front and rear drive axle

torque and the friction brake torque that are calculated from

the MATLAB Simulink model are transmitted to the ADAMS

model, 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 driver

excessively maneuvers the steering wheel, the rear-tire slip

angle may exceed its limit value, which results in reduced rear

lateral forces. This causes the vehicle sideslip angle and the yaw

rate to increase. The grip is lost, and consequently, steerability

becomes out of control. Therefore, to ensure vehicle stability,

an appropriate vehicle safety enhancement system should be

provided 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 =

F x = F xfr + F xfl + F xrr + F xrl (10)

mV (β̇ + γ ) =

F y = F yfr + F yfl + F yrr + F yrl (11)

I zγ̇ =

M z = (F xfr + F xfl) · Lf

− (F xrr + F xrl)Lr + M (12)

M = −w2

(F xfr − F xfl + F xrr − F xrl) (13)

where F is the tire force, I z is the moment of inertia, L is the

wheel base, M is the direct yaw moment that is generated from

the tire force at each wheel, V is the vehicle velocity, β is the

sideslip angle, γ is the yaw rate, m is the vehicle mass, w is

the vehicle tread, x is the longitudinal direction, y is the lateral

direction, 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 the

rear left wheel.

In vehicle stability control, the direct yaw moment M is used

as the control input of the system, while the tire force F at each

wheel, sideslip angle β , and yaw rate γ are calculated from theADAMS model.

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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 the

closed-loop simulation. Fig. 6 shows a schematic diagram of

the steering driver model.

The steering driver model manipulates the steering angle

to compensate the error between the estimated position and

the desired position. The estimated position x∗ and y∗ can be

calculated from the following equations [12]:

x∗ = x + (V x cosψ − V y sinψ) · Llook

V (14)

Fig. 9. Membership function for the fuzzy controller.

TABLE IRULE BASE FOR THE FUZZY CONTROLLER

y∗ = y + (V x sinψ + V y cosψ) ·Llook

V (15)

e =

(xd − x∗)2 + (yd − y∗)2 (16)

δ = PID(s) · e · exp(−τ δs) (17)

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

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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 the

steering angle, ψ is the vehicle heading angle, e is the error

of the displacement between the estimated position and the

desired position, Llook is the look ahead distance, PID(s)is the PID control gain, and τ δ is the human-response-time

constant for steering. Equation (17) is proposed to describe

the driver’s response, which manipulates the steering angle δ

that corresponds to the position error e. In (17), τ δ = 0.3 is

used by considering the average human response delay time for

perception [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 steering

angle δ , the desired yaw rate γ d and the desired sideslip angle

β d can be obtained as follows [12]:

γ d =1

1 + As · V 2·V

L· δ (18)

β d =1 − m

2L·

Lf

LrC rV 2

1 + As · V 2·Lr

L· δ (19)

As =m

2L2·LrC r − Lf C f

C f · C r(20)

where γ d is the desired yaw rate, β d is the desired sideslip angle,

As is the steering stability factor, C f is the front-tire corneringstiffness, and C r is the rear-tire cornering stiffness.

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D. Fuzzy Control Algorithm

For the vehicle stability control, a fuzzy control algorithm

is used by considering the tire nonlinear characteristics in

cornering [14]–[16]. The inputs of the fuzzy controller are

the errors of the vehicle sideslip angle and the yaw rate. The

error is defined as the difference between the desired value

from the desired vehicle model and the actual value from the

actual vehicle model. Using these inputs, the fuzzy controller

generates the direct yaw moment that is required to compensate

the errors.

In Figs. 7 and 8, a block diagram and a flowchart for the ve-

hicle stability control are shown. For the desired displacements

xd and yd, the driver model manipulates the steering angle δ .

The actual sideslip angle β and yaw rate γ are measured and

compared with the desired sideslip angle β d and yaw rate γ d,

which are calculated from the desired value estimator in (18)

and (19). β error and γ error are used as the inputs of the fuzzy

controller.

The fuzzy controller consists of a triangular membershipfunction (Fig. 9) that gives the direct yaw moment output for the

inputs of the yaw rate and sideslip angle errors. The rule base

used in the fuzzy controller is shown in Table I. The rule base

consists of the five linguistic variables—negative big (NB), neg-

ative small (NS), zero (ZR), positive small (PS), and positive

big (PB)—and is arranged by the center-of-gravity method [17].

The fuzzy controller calculates the direct yaw moment M to

compensate the errors. The direct yaw moment M is used

as the system control input. In generating the required direct

yaw moment M , the following control strategy is proposed

to maximize the recapturing energy and fast response: M is

generated by the rear motor driving and regenerative brakingcontrols, in priority, and if the direct yaw moment by the rear

motor control is not sufficient enough, M is compensated by

the EHB force at the front and rear wheels.

Fig. 10 shows how the yaw moment is generated by the rear

motor and EHB module with respect to the yaw rate error.

When the yaw rate error eγ becomes negative, the vehicle

shows oversteer characteristics, and vice versa. For the over-

steer case [Fig. 10(a)], the rear motor is controlled to carry out

the 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 results

in the decreased slip in the longitudinal direction. This causes

increased 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 yaw

moment in the opposite direction, which operates to reduce the

sideslip angle and the yaw rate.

If the direct yaw moment by the regenerative braking is not

large enough to control β and γ , the EHB module begins to

come into action, together with the regenerative braking.

In the case of understeer [Fig. 10(b)], the rear motor is

controlled to provide tractive force, which generates the direct

yaw moment to assist the vehicle cornering motion. When the

tractive force is applied at the rear wheel, the lateral force

on 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 reduces

understeer. 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 carried

out for a J-turn and a single-lane change. Table II lists the

vehicle parameters used in the simulations.

A. J-Turn Simulation

Fig. 11 shows the simulation results for the J-turn [18]. In

the 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, simulation

results of (b) the yaw rate, (c) yaw rate error, (d) sideslip

angle, and (e) vehicle trajectory are shown. In vehicle dynamic

control, 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 is

applied, which causes the vehicle to spin (e) in the counter-

clockwise direction. In the case of the rear motor control (Motor

only), the sideslip angle, yaw rate, and vehicle trajectory follow

the targets, showing some errors. The vehicle attitude shows

some spin, but it is noted that the amount of spin is reduced

a lot when compared to that of No control. From Fig. 11,it is found that vehicle stability can be improved only by the rear

motor control. To achieve better performance, the EHB must

be applied at the right side of the wheels. Simulation results

using the EHB are shown in Fig. 11. In the simulation, the

rear motor control is applied with the EHB, and the braking

force by the EHB module is applied only for the right-side

wheels to generate the required direct yaw moment. As shown

in Fig. 11, the sideslip angle and yaw rate for the rear motor

control with EHB (Motor + EHB) follow the control targets,

showing reduced errors when compared to those of the Motor

only. Correspondingly, the vehicle trajectory (e) follows the

target trajectory closely, while the vehicle attitude is maintained

without spin. When the yaw rate error (c) becomes positive, themotor generates the tractive force needed to reduce understeer.

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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 carries

out the regenerative braking to reduce oversteer. As shown in

Fig. 11(f), the dynamic behavior of the vehicle for each case

can 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 at

80 km/h constant velocity under the slippery road condition

of µ = 0.2. Fig. 12 shows (b) the yaw rate, (c) the yaw rate

error, (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 show

improved response, but they still have some errors in following

the target value. It is noted that the vehicle stability control

with Motor + EHB follows the target value most closely. As

shown in Fig. 12(c), the yaw rate error shows either a positive

or negative value, which means that the vehicle experiences the

understeer or oversteer motion. Corresponding to the yaw rate

error, the rear motor generates the tractive force or regenera-

tive braking force, respectively. The dynamic behavior of the

vehicle for a single-lane change can be monitored by using the

ADAMS 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

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performance. Compared to the EHB-only braking, the vehicle

stability enhancement algorithm using the regenerative braking

plus EHB is able to provide improved vehicle stability and

additional improvement in fuel economy due to regenerative

braking.

V. CONCLUSION

Vehicle stability control for a 4WD HEV has been investi-

gated using rear motor and EHB controls. A fuzzy-rule-based

control algorithm was proposed, which generates the direct yaw

moment to compensate for the errors of the sideslip angle and

the yaw rate between the outputs of the desired value estimator

and the actual vehicle model. Performance of the vehicle stabil-

ity control algorithm is evaluated using ADAMS and MATLAB

Simulink cosimulations. The ADAMS model calculates the

actual vehicle behavior such as the yaw rate, sideslip angle,

lateral acceleration, and vehicle velocity by considering the tire

nonlinearity, suspension characteristics, and steering system.

The MATLAB Simulink model calculates the axle torque by

the rear motor and the EHB force at each wheel from the power

train model and the control logic. It is found from the simulation

results that the direct yaw moment generated by the rear motor

control is able to provide improved stability compared with the

vehicle performance without any control. In addition, better

performance can be achieved by applying the rear motor plus

the EHB control. It is expected that the vehicle stability control

algorithm suggested in this paper is able to offer an additional

improvement 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 Researcher

with 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 of Science 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, Korean

Society 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.

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