research on system control and energy management ...harbin institute of technology, harbin, china....

100 CES TRANSACTIONS ON ELECTRICAL MACHINES AND SYSTEMS, VOL. 1, NO. 2, JUNE 2017

Abstract—The flux-modulated compound-structure permanent

magnet synchronous machine (CS-PMSM), composed of a

brushless double rotor machine (DRM) and a conventional

permanent magnet synchronous machine (PMSM), is a power

split device for plug-in hybrid electric vehicles. In this paper, its

operating principle and mathematical model are introduced. A

modified current controller with decoupled state feedback is

proposed and verified. The system control strategy is simulated in

Matlab, and the feasibility of the control system is proven. To

improve fuel economy, an energy management strategy based on

fuzzy logic controller is proposed and evaluated by the Urban

Dynamometer Driving Schedule (UDDS) drive cycle. The results

show that the total energy consumption is similar to that of Prius

2012.

Index Terms—CS-PMSM, energy management strategy,

flux-modulated, hybrid electric vehicle, system control.

I. INTRODUCTION

N recent years, electric vehicles (EVs) and hybrid electric

vehicles (HEVs) have drawn wide attention[1]. The plug-in

hybrid electric vehicle (PHEV) is installed with a larger battery

compared with conventional full-hybrid HEVs, enabling longer

distance of pure electric mode operation with less emissions.

To achieve optimal energy distribution, a power split device is

required, linking the internal combustion engine (ICE),

generator and electric motor together. At present, the planetary

gear system used in Toyota Prius is a most mature

power-splitting scheme. However, as a pure mechanical device,

the planetary gear set has problems of vibration, noise and

This work was supported by National Natural Science Foundation of China

under Project 51325701, 51377030, and 51407042.

Jiaqi Liu is with school of Electrical Engineering and Automation, Harbin

Institute of Technology, Harbin, China. ([email protected])

Chengde Tong is with school of Electrical Engineering and Automation,

Harbin Institute of Technology, Harbin, China. ([email protected])

Zengfeng Jin is currently working in SAIC Motor, Shanghai, China.

([email protected])

Guangyuan Qiao is with school of Electrical Engineering and Automation,

Harbin Institute of Technology, Harbin, China. ([email protected])

Ping Zheng is with school of Electrical Engineering and Automation, Harbin

Institute of Technology, Harbin, China. ([email protected])

abrasion[2]. To solve these problems, researchers have

proposed various pure electrical schemes based on

compound-structure electric machines[3-6]. However, most of

the schemes have brushes, with problems of low reliability of

brushes and difficult cooling of the inner rotor, limiting their

applications.

A brushless electrical scheme based on the magnetic field

modulation principle named flux-modulated

compound-structure permanent magnet synchronous machine

(CS-PMSM) was proposed, as shown in Fig.1. It is composed

of a brushless double-rotor machine (DRM) and a conventional

permanent magnet synchronous machine (PMSM). The

brushless DRM has two rotors. One is the PM rotor, the other is

the modulating ring rotor, which is formed by the alternant

placement of magnetic and non-magnetic blocks. The

modulating ring rotor is connected to ICE, while PM rotor-1 is

coupled with PM rotor-2 which is connected to final drive. The

brushless DRM provides the speed difference between ICE and

wheels, and transmits the torque of ICE in a certain proportion.

Motor-2, formed by stator-2 and PM rotor-2, provides the

torque difference between ICE and wheels. Therefore, the ICE

can work in high efficiency area regardless of HEV’s operating

condition[7,8]. Without brushes, the problems in those pure

electrical schemes with brushes are solved.

Final drive

InverterBattery

pack

ICE

Modulating

ring rotor

Stator-1 Stator-2

PM rotor-1

PM rotor-2

Fig. 1. Schematic diagram of a hybrid electric drive system based on magnetic

field modulation principle.

Research on System Control and Energy

Management Strategy of Flux-Modulated

Compound-Structure Permanent Magnet

Synchronous Machine

Jiaqi Liu, Chengde Tong, Member, IEEE, Zengfeng Jin, Guangyuan Qiao, Ping Zheng, Senior Member,

IEEE

(Invited)

I

LIU et al. : RESEARCH ON SYSTEM CONTROL AND ENERGY MANAGEMENT STRATEGY OF FLUX-MODULATED 101

COMPOUND-STRUCTURE PERMANENT MAGNET SYNCHRONOUS MACHINE

Unlike conventional PMSMs, the brushless DRM works on

the magnetic field modulation principle. The matching of the

pole pair numbers, the wide speed range of the input and the

output lead to its wide current frequency range. Because of its

wide frequency range, the couplings between the d- and q- axis

current control should be considered. In this paper, an improved

control strategy of the hybrid electric drive system with the

decoupled current controller is proposed. An energy

management strategy based on the brushless DRM system is

proposed. The fuzzy logic brake controller is designed to

achieve the maximum energy recycling. Then the system model

is built and evaluated by the Urban Dynamometer Driving

Schedule (UDDS) drive cycle.

II. THE HYBRID ELECTRIC DRIVE SYSTEM BASED ON

MAGNETIC FIELD MODULATION PRINCIPLE

A. Principle of Flux-Modulated CS-PMSM

The operating principle of the brushless DRM follows the

magnetic field modulation principle[7]. On the basis of that, the

pole pair numbers of stator-1, PM rotor-1 and magnetic blocks

satisfy,

S PM Rp p N (1)

where pS and pPM are the pole pair numbers of stator-1 and PM

rotor-1, respectively, NR is the number of the magnetic blocks

of the modulating ring rotor.

To generate steady torque, the speeds of the stator magnetic

field, the PM rotor and the modulating ring rotor can be

expressed as,

S S PM PM R Rp p N (2)

where ΩS, ΩPM and ΩR are the rotating speeds of the stator

magnetic field, the PM rotor and the modulating ring rotor,

respectively. Meanwhile, the torques of the stator, the PM rotor

and the modulating ring rotor can be expressed as,

SR PM

R S PM

- =TT T

N p p (3)

where TR, TS and TPM are the electromagnetic torques of the

modulating ring rotor, the stator and the PM rotor.

B. Mathematical Models of Flux-Modulated CS-PMSM

To simplify the analysis, saturation, eddy currents and

hysteresis losses are neglected.

Flux linkage equations can be expressed as,

d1 d1 d1 f1

q1 q1 q1

d2 d2 d2 f2

q2 q2 q2

L i

L i

L i

L i

(4)

where Ψd1, Ψq1, Ψd2, Ψq2 are the d- and q- axis flux linkages of

stator-1 and stator-2; Ψf1 andΨf2 are the flux linkages produced

by PM rotor-1 and PM rotor-2; Ld1, Lq1, Ld2, Lq2 are the d- and q-

axis inductances of stator-1 and stator-2; id1, iq1, id2, iq2 are the d-

and q- axis currents of stator-1 and stator-2, respectively.

Voltage equations of the CS-PMSM can be expressed as,

d1 1 d1 d1 R R PM1 L q1

q1 1 q1 q1 R R PM1 L d1

d2 2 d2 d2 PM2 L q2

q2 2 q2 q2 PM2 L d2

p ( )

p ( )

p

p

u R i N Ω p Ω

u R i N Ω p Ω

u R i p Ω

u R i p Ω

(5)

where ud1, uq1, ud2, uq2 are the d- and q- axis voltages of stator-1

and stator-2; R1 and R2 are the winding resistances of stator-1

and stator-2; p is differential operator; pPM1 and pPM2 are the

pole pair numbers of PM rotor-1 and PM rotor-2;

Without electromagnetic coupling between the brushless

DRM and motor-2, the electromagnetic torque generated by

stator-1 and motor-2 can be calculated independently,

S1 S1 q1 d1 S1 d1 q1

M2 PM2 q2 d1 PM2 d2 q2

3( )

2

3( )

2

T p i p i

T p i p i

(6)

where TS1 and TM2 are the electromagnetic torque generated by

stator-1 and motor-2, respectively. Then, the motion equations

can be expressed as,

R RR S1 ICE R

S1

L PM1PM S1 M2 L PM

S1

d

d

d

d

Ω NJ T T R

t p

Ω pJ T T T R

t p

(7)

where JR and JPM are the moments of inertia of the modulating

ring rotor and PM rotor-2; TICE and TL are torques provided by

ICE and final drive; RR and RPM are the resistance functions of

input rotor (i.e., the modulating ring rotor) and output rotor (i.e.,

PM rotor-1 and PM rotor-2), respectively.

C. System Control Diagram

The control diagram of the flux-modulated CS-PMSM

system is shown in Fig.2.

3-phase

inverter

3-phase

inverter

En

erg

y m

an

ag

em

ent

un

itA

ccele

rato

r

Bra

ke

SO

C

Connected

to ICE

Connected

to final drive

CS-PMSM

control unit

L

a2i

b2i

DCu

L

*

LT

*

R

R

a1i

b1i

R

PWM

1-6

PWM

7-12

Fig. 2. Control diagram of the CS-PMSM system.

According to the state of charge (SOC), the vehicle speed

and the signals from brake and accelerator, the energy

management unit can calculate the state of vehicle, determine

the optimal operating condition of the ICE, and provide

CS-PMSM control unit’s command.


III. CONTROL OF HYBRID ELECTRIC SYSTEM

A. Control of Brushless DRM

In (5), there are cross couplings related to rotating speed of

stator magnetic field and stator inductances. Usually,

conventional control strategies treat them as disturbance signals.

The cross couplings have little effects on system control at low

frequency. But the effects can’t be ignored at high frequency.

Because of the brushless DRM’s wide speed range of stator

magnetic field, it is important to decouple the current controller

for its rapidity and stability.

Assume that ωR=NRΩR, ωPM=NPM1ΩL. According to (4) and

(5), the current control diagram of the brushless DRM based on

the conventional PI controller is shown in Fig.3.

Kp

*

d1i-

+Ki/Kp 1/p

+

+

d1u

R1

++

-

1/p1/Ld1

R PM q1( )L

d1i

Kp

*

q1i

-+ Ki/Kp 1/p

+

+ q1u

+-

-

1/p1/Lq1

1qi

R PM d1( )L

R1

f1 R PM

-

Fig. 3. Current control diagram of the brushless DRM based on conventional PI

controller.

To simplify the analysis, voltage equations expressed by

complex vectors are employed. Assume that mapping from d-

and q- axis components to complex vectors are given by[9],

qd q djf f f (8)

where fqd is the variable (e.g. u, i) expressed by complex vectors,

fq and fd are the d- and q- axis components.

By the mapping (8), the voltage equation of the brushless

DRM can be expressed as,

qd1 1 qd1 R PM q1 q1 d1 d1p j( ) ju R i L i L i

R PM f1( ) (9)

For flux-modulated brushless DRMs, Ld1=Lq1=L1[10].

Therefore, (9) can be simplified as,

qd1 1 qd1 1 R PM qd1 R PM f1p j( ) ( )u R i L i (10)

The current control diagram of the brushless DRM with

complex vectors is shown in Fig.4.

Kp

*

qd1i

-+ Ki/Kp 1/p

+

+ qd1u

+ -

-1/p1/L1

qd1i

R PM 1j( )L

R1

f1 R PM

-

Fig. 4. Current control diagram of the brushless DRM with complex vectors.

Usually, PWM inverters are equivalent to first order inertia

elements, whose time constants are the periods of the PWMs.

The effects on system control produced by PWM inverters are

ignored when the switch frequency is very high. Then the open

loop transfer function can be expressed as,

ip

p

11 R PM

1

( )

j( )

KK s

KG s

RsL s

L

(11)

Considering that the controller is a first order system,

conventional PI current controllers regard the couplings as

disturbances. The imaginary parts of the poles are ignored.

When Ki/Kp=R1/L1, pole-zero cancellation makes the system

steady. The ignored j(ωR-ωPM)L has minor effects on system

control at low frequency. However, the effects can’t be ignored

at high frequency. To extend the frequency range and enhance

the stability of the system, controller correction is necessary. A

common way is to introduce a positive feedback, as shown in

Fig.5.

Kp

*

qd1i

-+

Ki/Kp 1/p

+

+ qd1u

+-

-

1/p1/L1

qd1i

R PM 1j( )L

R1

f1 R PM

-

R PM 1j( )L

+

Fig. 5. Current control diagram with decoupled state feedback.

The open loop transfer function can be expressed as,

ip

p

11

1

( )

KK s

KG s

RsL s

L

(12)

By PI adjustment, the effects can be eliminated with

pole-zero cancellation.

However, this method requires d- and q- axis inductances in

advance. Large parameter error will affect the performance

greatly. Another system correction method is to introduce an

imaginary zero, as shown in Fig.6, realizing the pole-zero

cancellation as well.

Kp

*

qd1i

-+ Ki/Kp 1/p

+

+ qd1u

+-

-1/p1/L1

qd1i

R PM 1j( )L

R1

f1 R PM

-

+

R PMj( )

+

+

Fig. 6. Modified current control diagram with imaginary zero introduction.

The open loop transfer function can be expressed as,

ip R PM

p

11 R PM

1

j( )

( )

j( )

KK s

KG s

RsL s

L

(13)



0.04 0.06 0.08 0.10 0.12 0.14

0

1

2

3

4

5

id(A

)

Time (s)

0.04 0.06 0.08 0.10 0.12 0.14

0

1

2

3

4

5

Command

Output

Command

Output

iq(A

)

Time (s)

(a) Conventional

0.04 0.06 0.08 0.10 0.12 0.14

0

1

2

3

4

5

Command

Output

Command

Output

id(A

)

Time (s)

0.04 0.06 0.08 0.10 0.12 0.14

0

1

2

3

4

5

iq(A

)

Time (s)

(b) Modified

Fig. 7. Step response of PI current controller when (ωR-ωPM)/2π=200Hz.

B. System Simulation

The reference torques of the brushless DRM and motor-2 are

given by Fig.8.

PI

+

-

*

LT

*

R

*

M2T

*

S1T

PM1

R

P

N

S1

R

P

N

R

+

-

Fig. 8. Control diagram of CS-PMSM system

The system model is built in Matlab/Simulink. The pole pair

numbers of stator-1 and PM rotor-1 are 4 and 17, respectively,

and the magnetic block number of the modulating ring is 21.

Then the system is simulated in two different modes. One is the

hybrid driving mode, the other is the ICE regulation mode.

Fig.9 shows the speeds and torques of the system working in

hybrid driving mode, which keeps the operating point of the

ICE fixed and changes the load. Fig.10 shows the speeds and

torques of the system working in ICE regulation mode, which

keeps the load fixed and changes the operating point of the ICE.

0 1 2 3 4 5-6000

-4000

-2000

0

2000

4000

6000

Time (s)

Sp

eed

(rp

m)

L

ICE

S1

0 1 2 3 4 5-60

-40

-20

0

20

40

60

80

Time (s)

To

rqu

e (

N·m

)

TL

TICE

TM2

(a) Speeds (b) Torques

Fig. 9. Speeds and torques in hybrid driving mode

0 0.5 1 1.5 2-6000

-4000

-2000

0

2000

4000

6000

Time (s)S

pee

d (

rpm

)

L

ICE

S1

0 0.5 1 1.5 2-60

-40

-20

0

20

40

60

80

Time (s)

To

rqu

e (N

·m)

TL

TICE

TM2

(a) Speeds (b) Torques

Fig. 10. Speeds and torques in ICE regulation mode.

According to Fig.9, when load changes, the operating point

of the ICE keeps unchanged. According to Fig.10, the change

of ICE operating point won’t affect the output. It indicates that

the flux-modulated CS-PMSM decouples the speeds and

torques between the ICE and the load. Speeds are decoupled by

speed regulation of the brushless DRM, and the torques are

decoupled by torque regulation of motor-2.

IV. ENERGY MANAGEMENT STRATEGY

The energy management strategy realizes the management

and distribution of the system energy, which is important for

dynamic and economic performances of the vehicle. The

energy management strategy requires: 1) meeting drive

requirements (reflected by accelerator and brake); 2) keeping

the SOC in a reasonable range, neither overcharge nor

overdischarge; 3) reducing fuel consumption and emissions.

The flux-modulated CS-PMSM system is a new type of power

split device for HEVs. Its energy management strategy is

investigated in this paper. Considering that the HEV is a

multivariable nonlinear system, it is hard to build a precise

mathematic model. Therefore, the fuzzy logic control, which is

based on experience and insensitive to parameter variation, is

employed in the energy management strategy, making the

system robust and easy to control[11,12].

A. Design of Energy Management Fuzzy logic Controller

a) Design of Fuzzy logic Drive Controller

Compared with conventional vehicles, the HEVs realize the

decoupling of ICE and output. When the vehicle drives, the

fuzzy logic controller decides the state of ICE on the basis of


drive requirements, SOC and vehicle speed, making the ICE

most efficient.

Fig. 11. Schematic diagram of fuzzy logic drive controller.

The fuzzy logic drive controller is shown in Fig.11. The

inputs are SOC, vehicle speed and signal from accelerator, and

the output is the speed of ICE. Every input has five fuzzy sets:

VL, L, M, H and VH, as shown in Fig.12 (a), (b) and (c). The

output has seven sets for precise control of ICE operating point:

VVL, VL, L, M, H, VH, VVH, as shown in Fig.12 (d).

(a) SOC

(b) Vehicle speed

(c) Accelerator

(d) ICE speed

Fig. 12. Membership functions of fuzzy logic drive controller

Control rules are the core of fuzzy logic controller, reflecting

the intention of controller. The rules are based on following

ideas:

(1) When the vehicle speed is low and the SOC is high, shut

down the ICE. The vehicle is driven by the motor alone.

(2) In different speed ranges, the SOC and the drive torque

requirement decide the operating mode.

(3) When the vehicle speed is high, the vehicle works in

hybrid drive mode.

The relation between the input and output of the fuzzy logic

drive controller is shown in Fig.13 (a) and (b).

(a) ICE speed versus SOC and vehicle speed when accelerator equals 0.5

(b) ICE speed versus SOC and accelerator when vehicle speed equals 0.5

Fig.13. Relation between the input and output of the fuzzy logic drive

controller.

b).Design of Fuzzy logic Brake Controller

HEVs can work in three brake modes: mechanical brake,

electromagnetic brake and hybrid brake. When the vehicle

brakes, the requirements of security and reliability should be

considered firstly, and the energy should be recycled

maximally. The fuzzy logic controller decides the brake torque

distribution between mechanical brake and electromagnetic

brake on the basis of brake requirement, SOC and vehicle

speed.

The fuzzy logic brake controller is shown in Fig.14. The

inputs are SOC, vehicle speed and signal from brake. Their

membership functions are shown in Fig.12 (a), Fig.12 (b) and

Fig.15 (a). The output is brake factor Kd, which is the ratio of



the motor braking torque to the maximum motor torque. The

membership function of it is shown in Fig.15 (b).

The rules of the fuzzy logic brake controller are based on

following ideas:

(1) When the SOC is high, Kd is small.

(2) When the SOC is low, Kd varies with vehicle speed and

brake torque requirement.

(3) As the SOC goes up, Kd decreases gradually.

The relation between the input and output of the fuzzy logic

brake controller is shown in Fig.16 (a) and (b).

Fig. 14. Schematic diagram of fuzzy logic brake controller.

(a) Brake

(b) Kd

Fig. 15. Membership functions of fuzzy logic brake controller.

(a) Kd versus brake and vehicle speed when SOC equals 0.5

(b) Kd versus SOC and vehicle speed when brake equals 0.5

Fig. 16. Relation between the input and output of the fuzzy logic brake

controller.

B. Simulation of Energy Management Strategy

The model is built in Cruise and the Urban Dynamometer

Driving Schedule (UDDS) drive cycles are used to evaluate the

system performances. The choices of ICE, drive motor, battery

and vehicle parameters refer to those of Plug-in HEV Prius

2012. Parameters of the system are shown in TABLE I.

TABLE I

PARAMETERS OF SYSTEM

Units Parameters Value

ICE

Type Four-cylinder gasoline

engine

Displacement 1.8L

Maximum torque 172N·m

Motor

Max power 68kW

Max torque 201N·m

Max speed 8000r/min

Battery

Type NiMH

Capacity 4.4kWh

Power level 27kW

Brushless

DRM

Max power 10kW

pS 4

pPM 17

NR 21

Vehicle

Mass 1588kg

Final ratio 3.905

Windward area 1.745m2

Drag coefficient 0.3

Rolling resistance coefficient 0.009

Tire friction coefficient 0.95

Wheel radius 0.287m

Fuel density 0.76kg/L

Fuel calorific value 44000kJ/kg

The simulation of vehicle speed is shown in Fig.17. The

result shows that the vehicle speed follows the reference speed

well. Other simulation results are shown in Fig.18-21. It

indicates that when the SOC is high and the vehicle speed is

low, the ICE keeps closed, and the vehicle is driven by motor

alone; when the speed goes up, the ICE starts and the vehicle is

driven by the ICE and the motor together; when the SOC is

about 0.5, the battery works in battery maintenance state, when

the SOC goes down, start the ICE, when the SOC goes up, shut

down the ICE.

The operating points of the ICE are shown in Fig.22. The

blue curve, the red curve and the green curve are the maximum


torque curve, the optimal efficiency curve and the operating

curve of the ICE, respectively. It shows that the ICE always

works in optimal efficiency state.

The total driving distance is 48km, and the energy

consumption comparisons between the CS-PMSM and the

Prius 2012 system in same test conditions are shown in TABLE

II. It shows that the total energy consumption is 0.5% more than

that of the Prius 2012 system.

0 1000 2000 3000 4000 50000

20

40

60

80

100

reference speed actual speed

Vehic

le S

peed (

km

/h)

Time (s)

Fig. 17. Vehicle speed.

0 1000 2000 3000 4000 50000

5

10

15

0

50

0

1000

2000

power of ICE

Time (s)

Sp

eed

(rp

m)

Po

wer

(kW

)

torque of ICE

To

rqu

e (

Nm

)

speed of ICE

Fig. 18. Speed, torque and power of ICE.

0 1000 2000 3000 4000 5000-40

-20

0

20

-200

-100

0

100

200

0

1000

2000

3000

4000

power of motor

Time (s)

Speed (

rpm

)P

ow

er

(kW

)

torque of motor

Torq

ue (

Nm

)

speed of motor

Fig. 19. Speed, torque and power of motor.

0 500 1000 1500 2000 2500

-5

0

5

10

-10

0

10

-5000

0

5000

power of generator

Time (s)

Sp

eed

(rp

m)

Po

wer

(kW

)

torque of generator

To

rqu

e (

Nm

)

speed of generator

Fig. 20. Speed, torque and power of brushless DRM.

-20

-10

0

10

20

0 1000 2000 3000 4000 50000.0

0.2

0.4

0.6

0.8

1.0

Pow

er

(kW

)

SO

C

Time (s)

Fig. 21. Power and SOC of battery

Speed (rpm)

Torq

ue

(Nm

)

Fig. 22. Operating state of ICE.

TABLE II

Energy consumption CS-PMSM Prius 2012 system

Fuel consumption 1.51L 1.40L

Electricity consumption 1.57kWh 2.49kWh

Total energy consumption 56046.48kJ 55762.00kJ



V. CONCLUSION

This paper proposes an improved control strategy of the

flux-modulated CS-PMSM. To solve the problem of couplings

between d- and q- axis current control, a modified current

controller is proposed. Then the system control strategy is

simulated in different operating conditions. The results show

that the speeds and the torques of the ICE and the output are

decoupled. The energy management strategy based on the

brushless DRM system is proposed. To recycle the energy

maximally, the fuzzy logic brake controller is designed. The

results show that the ICE always works in optimal efficiency

state, the battery is controlled optimally and the total energy

consumption is similar to that of Prius 2012.

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[12] Abdelsalam A A, Cui S. “Fuzzy Logic Global Power Management Strategy for HEV Based on Permanent Magnet-Dual Mechanical Port Machine”, IEEE Power Electronics and Motion Control Conference (IPEMC), Harbin, 2012, pp. 859-866.

Jiaqi Liu, received the B.Sc. and M.Sc.

degrees in electrical engineering from

Harbin Institute of Technology, Harbin,

China, in 2014 and 2016, respectively,

where he is currently working towards the

Ph.D. degree.

His research interests include brushless

compound-structure permanent-magnet

synchronous machines used in hybrid

electric vehicles.

Chengde Tong (M’13) received the B.Sc.,

M.Sc., and Ph.D. degrees from Harbin

Institute of Technology, Harbin, China, in

2007, 2009, and 2013, respectively, all in

electrical engineering.

He is currently an Associate Professor

with the Department of Electrical

Engineering, Harbin Institute of

Technology. He is the author or coauthor of more than 40

published papers. His interests include electric drives and

energy management of hybrid electric vehicles, free-piston

Stirling engines, and permanent- magnet linear machines.

Zengfeng Jin, received the B.Sc. and

M.Sc. degrees in electrical engineering

from Harbin Institute of Technology,

Harbin, China, in 2014 and 2016,

respectively. He is currently working in

SAIC Motor, Shanghai, China.

His research interests include electric

drives and energy management of hybrid

electric vehicles.

Guangyuan Qiao, is currently working

towards the B.Sc degree in electrical

engineering in the school of Electrical

Engineering and Automation, Harbin

Institute of Technology, Harbin, China.

His research interests include drive and

control of permanent magnet machines.


Ping Zheng (M’04–S’05) received the

B.Sc., M.Sc., and Ph.D. degrees from

Harbin Institute of Technology, Harbin,

China, in 1992, 1995, and 1999,

respectively, all in electrical engineering.

Since 1995, she has been with Harbin

Institute of Technology, where she has

been a Professor since 2005. She is the

author or coauthor of more than 200 published refereed

technical papers and four books. She is the holder of 47 Chinese

invention patents. Her current research interests include electric

machines and control, hybrid electric vehicles, and

unconventional electromagnetic devices.

Dr. Zheng is a member of the IEEE Electric Machines

Committee, the IEEE Industrial Electronics Society, the IEEE

Industry Applications Society, the IEEE Standards Association,

and the International Compumag Society. She was a recipient

of more than 30 technical awards, including the “China Youth

Science and Technology Award” from the Organization

Department of the Communist Party of China in 2009, the

“National Science Foundation for Distinguished Young

Scholars of China” from the National Natural Science

Foundation of China in 2013, and “Yangtze River Scholar

Professor” from the Ministry of Education of China in 2014.

research on system control and energy management ...harbin institute of technology, harbin, china....

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