Research on System Control and Energy Management ...Harbin Institute of Technology, Harbin, China....
Transcript of Research on System Control and Energy Management ...Harbin Institute of Technology, Harbin, China....
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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.
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
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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.
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102 CES TRANSACTIONS ON ELECTRICAL MACHINES AND SYSTEMS, VOL. 1, NO. 2, JUNE 2017
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)
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LIU et al. : RESEARCH ON SYSTEM CONTROL AND ENERGY MANAGEMENT STRATEGY OF FLUX-MODULATED 103
COMPOUND-STRUCTURE PERMANENT MAGNET SYNCHRONOUS MACHINE
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
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104 CES TRANSACTIONS ON ELECTRICAL MACHINES AND SYSTEMS, VOL. 1, NO. 2, JUNE 2017
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
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LIU et al. : RESEARCH ON SYSTEM CONTROL AND ENERGY MANAGEMENT STRATEGY OF FLUX-MODULATED 105
COMPOUND-STRUCTURE PERMANENT MAGNET SYNCHRONOUS MACHINE
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
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106 CES TRANSACTIONS ON ELECTRICAL MACHINES AND SYSTEMS, VOL. 1, NO. 2, JUNE 2017
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
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LIU et al. : RESEARCH ON SYSTEM CONTROL AND ENERGY MANAGEMENT STRATEGY OF FLUX-MODULATED 107
COMPOUND-STRUCTURE PERMANENT MAGNET SYNCHRONOUS MACHINE
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.
REFERENCES
[1] Chan C C. “The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles”, Proceedings of the IEEE, vol. 95, no. 4, pp. 704-718, 2007.
[2] Chau K T, Chan C C. “Emerging Energy-Efficient Technologies for Hybrid Electric Vehicles”, Proceedings of the IEEE, vol. 95, no. 4, pp. 821-835, 2007.
[3] Liu Y, Tong C D, Liu R R, et al. “Comprehensive Research on Compound-Structure Permanent-Magnet Synchronous Machine System Used for HEVs”, IEEE Energy Conversion Congress and Exposition (ECCE), Atlanta, 2010, pp. 1617-1622.
[4] Cai H W, Xu L Y. “Modeling and Control for Cage Rotor Dual Mechanical Port Electric Machine-Part I: Model Development”, IEEE Transactions on Energy Conversion, vol. 30, no. 3, pp. 957-965, 2015.
[5] Cai H W, Xu L Y.” Modeling and Control for Cage Rotor Dual Mechanical Port Electric Machine-Part II: Independent Control of Two Rotors”, IEEE Transactions on Energy Conversion, vol. 30, no. 3, pp. 966-973, 2015.
[6] Cui S M, Xu Q W, Cheng Y. “Research on Direct Torque Control for the Electrical Variable Transmission”, IEEE Vehicle Power and Propulsion Conference, Lille, 2010, pp. 1-5.
[7] Zheng P, Bai J, Tong C, et al. “Investigation of a Novel Radial Magnetic-Field-Modulated Brushless Double-Rotor Machine Used for HEVs”, IEEE Transactions on Magnetics, vol. 49, no. 3, pp. 1231-1241, 2013.
[8] Zheng P, Tong C, Bai J, et al. “Modeling and Control of a Flux-Modulated Compound-Structure Permanent-Magnet Synchronous Machine for Hybrid Electric Vehicles”, Energies, vol. 5, no. 1, pp. 45-57, 2012.
[9] Briz F, Degner M W, Lorenz R D. “Dynamic Analysis of Current Regulators for AC Motors Using Complex Vectors”, IEEE Transactions on Industry Applications, vol. 35, no. 6, pp. 1424-1432, 1999.
[10] Liu Y, Bai J, Fu Z, et al. “Design Method of a Magnetic-Field-Modulated Brushless Double-Rotor Machine Used for HEVs”, IEEE Transportation Electrification Asia-Pacific, Beijing, 2014, pp. 1-6.
[11] Li S G, Sharkh S M, Walsh F C, et al. “Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic”, IEEE Transactions on Vehicular Technology, vol. 60, no. 8, pp. 3571-3585, 2011.
[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.
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108 CES TRANSACTIONS ON ELECTRICAL MACHINES AND SYSTEMS, VOL. 1, NO. 2, JUNE 2017
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.