a novel coordination control of plug-in 4wd hybrid ... novel coordination... · a novel...

EVS28 International Electric Vehicle Symposium and Exhibition 1

EVS28 KINTEX, Korea, May 3-6, 2015

A Novel Coordination Control of Plug-in 4WD Hybrid Electric Vehicle Using Fuzzy PID

Lijun Qian Lihong Qiu School of Mechanical and Automotive Engineering, Hefei University of Technology, Hefei 230009, Anhui, China

Abstract The plug-in 4WD hybrid electric vehicle researched in this paper has 3 power components, a CVT, and dual

clutch, thus it has many work modes and mode transitions. And there are many variables to be taken into

consideration for the coordination control of the vehicle. So the control strategy of the vehicle is very

complicated and it is difficult to guarantee the fuel economy and drivability at the same time. To solve this

problem, this paper takes the dynamic model of the power components and every work mode into

consideration. A diver model based on fuzzy PID is built to obtain the torque request coefficient which

reflects driver’s intentions and it is used to determine the drive modes preliminarily. A control strategy based

on optimal engine torque is adopted, in which the dynamic model for different work modes as well as the

torque distribution method is described. For the coordination during mode switches, a method which can be

summarized as ‘Preliminary torque distributions, engine dynamic torque estimation, clutch fuzzy-fuzzy PID

control and dual motor compensation’ is put forward. For engine dynamic torque estimation, a lookup

method based on the experimental data is presented; For clutch control, two fuzzy controllers and a fuzzy

PID controller whose aim is to have a more precise output of the oil pressure of the clutch is proposed.

Afterwards, the control strategy is validated on the test bench, and the results verify that the power

components work cooperatively with each other in every mode and during mode transitions. Moreover, the

fuel consumption is reduced by 9.04%, and the jerk is reduced by 40.7%.

Keywords: PHEV, control strategy, dynamic model; engine torque estimation, fuzzy-fuzzy PID control；HIL bench test

1 Introduction Plug-in hybrid electric vehicle (PHEV) derives from hybrid electric vehicle, it is a very significant transitional product between the traditional internal combustion engine vehicle and pure electric vehicle (PEV), which plays a very important role in transportation and has a very great potential before the inherent defects of the battery is conquered to guarantee the PEV a longer driving range and a better reliability [1-2].

Plug-in four-wheel-drive hybrid electric vehicle (4WD PHEV) is a PHEV that can be driven by the 2 axles at a proper time, which can provide the PHEV with better power performance and passability. But the fuel economy may not be as good as the single axle driven vehicle, and the control of the vehicle is more complicated. So a trade-off is needed in the architecture and the control to minimize the fuel consumption with the power performance guaranteed [3-4]. With the layout confirmed by the Chery auto company, the problem left to be solved


is the control strategy of the vehicle. There exist 3 power components which can work independently or cooperatively for a 4WD PHEV, whose power system is very complicated. The coordination control involves many subjects and it is an intricate project needs to be improved continually. So, the research into the coordination control of the 4WD PHEV is of great significance. Coordination control for a hybrid electric vehicle includes the two aspects of the steady state and dynamic state. The coordination control during steady state is the energy management coordination control, which aims at torque distributions and the control strategy based on the logic threshold is widely used. While the coordination control during dynamic state mainly involves the coordination of the power components when mode changes. The coordination during mode switch can be classified into 2 categories; the first is based on the estimation of engine dynamic torque and the compensation of the drive motor; the second is based on hybrid dynamic system and other optimal methods. Currently, the algorithms for energy management coordination control include the rule-based strategies, the optimization-based strategies and intelligence-based strategies. The objective of the PHEV control is to find out the optimal torque distributions at every instant of time to minimize the fuel consumption with the power performances ensured over a giving driving schedule [5] which can be fully realized through global optimization methods. Dynamic programming and other approximate global optimal algorithms were proposed. But the prior knowledge was needed to get the profile of the roads, which was a big obstacle for the real time employment [6-7]. Although a lot of work has been done to do research into the optimization-based strategies and Intelligence- based strategies and a lot of improvements have been made, there is still a long way to go before putting them into practice for their inherent problems. Therefore, the rule-based strategies are still the priority in real industry. The rule-based control strategies consist of the logic threshold strategy and fuzzy logic strategy. The logic threshold strategy enjoys a good robustness and high executive efficiency but it cannot adapt to different driving cycles and the fuel economy cannot be guaranteed consequently. While fuzzy logic strategy is adaptive to various conditions with the experts’ knowledge and it doesn’t rely on accurate mathematic model, which

can bring about better fuel economy but the executive efficiency is reduced. However, the combination of the logic threshold strategy and fuzzy logic strategy can achieve better results [8-9]. For the coordination control during mode switches, Ping Kan, et al [10], described the basic theory for the cooperative work of the power components and during mode switch and the integrated frame work of the controller; Lijun Qian, et al [11], put forward the “Engine dynamic torque estimation & clutch fuzzy control & 2 motors compensation” method for a 4WD PHEV, in which they took the mode switch between EV to Parallel as an example and the jerk is greatly reduced. Sangjoon Kim, et al [12], Qin Datong, et al [13], Yang Yang, et al [14] put forward three different kinds of methods for the clutch control and made the rules during mode switches between EV to HEV for a parallel HEV, which obtained optimal clutch control under different conditions. Anthony Smith, et al [15],Yongsheng He, et al [16], Zhang Na, et al [17] put forward the closed-loop control strategy in combination of motor torque and clutch constant pressure for a parallel HEV to start the engine when it was EV mode, and the control strategy was validated on the bench test. Kerem Koprubasi, et al [18]，Liu Cui, et al [19]，Zhao Zhiguo, et al [20], K.Korowais, et al [21] regarded mode switches as the problem of a hybrid dynamic system or input-redundancy system and they classified the switches into different subdomains and designed controllers for them respectively. The control strategies were validated by experiments which proved them effective in reducing the jerk. R.beck et al [22], used model predictive method for the clutch control during mode switches which also reduced the jerk and the robustness was validated. Li Xian Yang et al [23] adopted dynamic programming for the smoothness during mode switch, which obtained the optimal torque trace of the engine, motor and the clutches. However, the hybrid dynamic system-based and other optimal-based methods are now in the lab and can’t be made full use of in the real car, and the estimation-compensation-based strategies still need to be improved for the inaccuracy brought in by the error of the controllers. In this paper, the coordination control for a plug-in 4WD PHEV is researched. The rest of the paper is organized as follows. In section 2, the configuration of the PHEV is briefly described and the dynamic models of the engine, motors, clutches and the powertrain are presented. In section 3, a driver model based on fuzzy PID is built to gain the torque request coefficient. In section 4, an energy


management coordination strategy based on driver intention is put forward. In which the control for the engine is based on its optimal torque and the dynamic model for different work modes as well as the torque distribution method is proposed. In section 5, the coordination control during mode switches is introduced. The emphases are laid on the clutch control and the dynamic torque obtainment of the engine. The coordination control algorithm is introduced by taking the transition between EV to Hybrid as an example. In section 6, the control strategy is validated hardware-in-loop on the test bench. At last the conclusions are presented in section 7.

2 Configuration of the PHEV The architecture for the 4WD PHEV is illustrated in Fig.1. It includes 2 separate power systems, of which a rear motor with MCU is included in the first power system, while an engine with ECU, an ISG motor with its controller which lies coaxially with the engine, a starter used to start the engine when battery SOC is low, a clutch 1 which lies between the engine and ISG motor, a clutch 2 which lies between the ISG motor and the CVT, and a CVT transmission are included in the second power system. The power cell is connected to the rear motor through inverter 1 and to the ISG motor through inverter 2[24].

ECU

Wheeel

Wheeel

Rear motor Inverter 2 Inverter 1

Power cellDC-DC Charger

ISG motor

Engine

CVT

Front axle

Wheeel

Wheeel

Starter

Rear axle

- +

12V battery

- +

Clutch 1

Plug

Vehicle CAN

Other controllersESP

VCU

Electrical connection

Mecahnical connection

Clutch 2MCUISG

controller

BMS

Fig.1 Layout of the 4WD PHEV

2.1 Engine model The engine used in the paper is a 4-cylinder gasoline engine (E4G16) without turbocharger, whose displacement is 1.6L, maximum power is 93kw, and maximum torque is 158 N.m. Engine model are of 2 kinds, steady type and dynamic type. When the engine is in steady state, its output torque is the function of the throttle opening and the speed whose model can be obtained through experimental data. When it is in dynamic state, the output torque can be obtained based on the steady

model with a second order transfer function shown in formula 1[25].

2

2 2( , )

2ω

ω αξ ω ω

=+ +

nee e e

e ne ne

T fs s

(1)

where eT is engine torque, N.m; ωe is engine speed, 1−⋅rad s , αe is throttle opening of the engine, %.

ωne is the natural frequency of the engine, ξe is the second order system damping ratio of the engine.

2.2 Motor model The ISG motor and rear motor adopted in the 4WD PHEV are permanent magnet synchronous AC motors with the maximum torque of 100 N.m and 120 N.m respectively. The steady models for the motors are also obtained through experiments while the dynamic model can be approximately expressed by a second order response system as presented in formula 2[26]. The motors work as drive motor when torque is greater than 0 and as generators when torque is less than 0.

2

max2 2

2

max2 2

min( , ) 02

max( , ) 02

nmmobj m mobj

m nm nmm

nmmobj m mobj

m nm nm

T T Ts s

TT T T

s s

ωξ ω ωω

ξ ω ω

⋅ > + +=

⋅ < + + (2） where mT is the motor torque, N.m; mobjT is motor target torque, N.m; maxmT is motor maximum torque, N.m; ωnm is the natural frequency of the motor,

1−⋅rad s ; ξm the second order system damping ratio of the motor.

2.3 Clutch model When the clutches are in the state of absolute engaged or disengaged, they transfer steady torque

[27], when they are still slipping, the transferred torque can be calculated by formula 3.

2 21 1 2 2

1 2

2( ) sgn( )3( )

µ ω+ +

= ∆+cl k n

r r r rT ZP Ar r

(3)

where clT is the transferred torque of the clutch, N.m; µk is the friction coefficient of the clutch; Z is the number of the friction plates; nP is the oil pressure, kpa; A is the area of the clutch friction plate, 2m ; ω∆ is the difference angular speed of active plate and passive plate of the clutch,

1−⋅rad s ; 1r is the inner radius of the friction plate, m; 2r is the outer radius of the friction plate, m.


2.4 Powertrain model The model of the powertrain can be simplified as Fig. 2 describes: The rotational inertia of the front end of the clutch 1 is equivalent as formula 4 describes:

1q e iJ J J= + (4)

EngineISG

Front final drive Vehicle

body&TireRear

final drive

Rear motor

Clutch1 Clutch2 CVT

Je J1i J1o

JISG

J2i J2o JtiJto Jof1

Jm Jof2

Jw+mr2

Fig.2 The simplified model of the powertrain

The rotational inertia of the rear end of the clutch 1 is equivalent as formula 5 presents:

2 2 20 1 01

2 2 1

( ) / /h f to w g

ti o i ISG o

J J J J mr i i

J J J J J

= + + + + + + + +

(5)

The rotational inertia between the front end and rear end of clutch 1 is equivalent as formula 6 shows:

1 2z o ISG iJ J J J= + + (6) The rotational inertia of the rear axle is equivalent as formula 7 shows:

2 22 02( ) /r m of wJ J J J mr i= + + + (7)

where eJ is engine rotational inertia, kg·m2; 1iJ is the rotational inertia for the input part of clutch 1;

ISGJ is the rotational inertia of ISG motor; 2iJ is the rotational inertia for the input part of clutch 2;

2oJ is the rotational inertia for the output part of clutch 2; tiJ is the input part rotational inertia of CVT; 0tJ is the output part rotational inertia of CVT; 0 1fJ is rotational inertia for front final drive;

wJ is equivalent rotational inertia of the vehicle; m is vehicle mass, kg; r is tire radius, m; mJ is rotational inertia for rear motor; 2ofJ is rotational inertia for rear final drive.

3 Driver Model Based on Fuzzy- PID

From classic control theory, it is known to all that it is very difficult to set the proportion, integration, and differentiation parameters for the traditional PID. While fuzzy controller can compensate the effect of the nonlinear factors and result in better dynamic characteristics, but the static errors can’t be eliminated. To overcome the defects of the traditional PID and fuzzy logic algorithms and reflect the driver’s intentions, a driver model based on fuzzy PID is built to calculate driver’s

torque request coefficient. And adopting fuzzy-PID can realize good dynamic and static performances

[28]. The driver model built in the paper is shown in Fig.3. It consists of 2 major parts, the first is a fuzzy-PID controller, and the second is a traditional PID controller. The fuzzy-PID controller contains 1 fuzzy controller and 1 PID controller. The inputs of the fuzzy controller are the difference of the cycle velocity and the output velocity of the vehicle, and the output of the PID controller are change value of the PID parameters, including δKp, δKi, δKd. From the fuzzy-PID controller the coefficient K of the driver’s torque request is obtained. K is adaptive to different cycles and can be used to reflect driver’s intention; the tradition PID controller in the driver model is used to calculate torque request T without considering the driver’s intentions. The actual required torque Treq is defined as the product of T and K.

KV T

PID1_Coefficient_K

1V

Treq V

Vehicle_model

v0 Tq

Resistance_torque

PID(s)

PID2_T0_Calculation

δKp

δKi

δKd

Kp

Ki

KdKp_Ki_Kd

1/s

Tq

KdKiKp

Fuzzy Logic Controller

Tq

Kd

Ki

Kp

du/dt

du/dt

1V0

Fig.3 Driver model based on fuzzy-PID

The fuzzy subsets of the inputs δV, dδV/dt and outputs δKp、δKi、δKd of the fuzzy controller in the driver model can be set as {NB,NM,NS,ZO, PM,PB}, which represent negative big, negative medium, negative small, 0, positive small, positive medium and positive big respectively. Taking the values of traditional controllers as references, the domains of the variables can be set as δV∈[−15,15],dδV/dt∈[−30,30],δKp∈[−18,18],δKi∈[−0.002,0.002],δKd∈[−1.5,1.5].Their membership functions are shown in Fig.4. And parts of the fuzzy rules are shown in table 1.

4 Coordination Control of Energy Management

The drive modes of the 4WD PHEV consists of rear-drive modes, front-drive modes, and 4WD modes, of which, the rear-drive modes contain the EV mode and Series mode, the front-drive modes contain engine-drive mode, ISG-drive mode, engine-drive-and-charge mode, ISG-aided-parallel mode, and 4WD modes contain rear-motor-aided- 4WD mode and all-hybrid-4WD mode [29].


-15 -10 -5 0 5 10 150

0.51

Velocity error/(km/h)

Mem

ersh

ip NB NM NS ZO PS PM PB

-30 -20 -10 0 10 20 300

0.51

Change rate of velocity error/(km/h/s）

Mem

ersh

ip

NB NM NS ZO PS PM PB

-15 -10 -5 0 5 10 150

0.51

Change of propotion coefficient

Mem

ersh

ip


-2 -1.5 -1 -0.5 0 0.5 1 1.5 2x 10-3

00.5

1

Change of integration coefficient

Mem

ersh

ip


-1.5 -1 -0.5 0 0.5 1 1.50

0.51

Change of differentiation coefficient

Mem

ersh

ip


Fig.4 Membership functions of the variables.

Table 1 Parts of fuzzy rules Velocity error/ (km·h-1

)

Change rate of velocity

error/(km·h-1·s-1)

Change of proportion coefficient

Change of integratio

n coefficien

t

Change of Differentia

tion coefficient

NB NM PB NB NS NB ZO PM NM NB NM NM PB NB NS NM NS PM NM NB NM PB NS ZO ZO NS ZO PS NS NM NS PB NS PS ZO ZO NB PM NM ZO ZO PS NS PS NS ZO PM NM PS NS

First of all, define that when the torque request coefficient K is between 0.8-0.95, it is small, when K is between 0.95-1.05，it is medium, and when K is between 1.05-1.2, it is big. And then the control flow of the drive modes for the 4WD PHEV is as follows: Step1. Judging the range of the torque request coefficient K ; when K is small, turn to step2; when K is medium, turn to step3; when K is big, turn to step 4. Step2. Execute rear-drive mode. Step2A. Judging whether SOC is greater than its lowest work point LOWSOC , if LOWSOC SOC> , execute step2A1, otherwise execute step2A2. Step2A1. Execute rear -drive EV mode, and return to execute step1. Then,

0m req r m

e ISG

T T JT T

ω − =

= =

(8)

where mT , eT , ISGT represent the output torque of the rear motor, the engine, and the ISG motor.

ori is the ratio of rear final drive.

Step2A2. Execute Series mode, and return to execute step1. Then,

m req r m

e ISG q e

ISG ch z ISG

T T J

T T J

T T J

ω

ω

ω

− =

− = − =

(9)

where eoptT represents the optimal torque of the engine, chT is the charging torque. Step3. Execute the front drive mode. Step3A. Judging whether SOC is greater than its lowest work point LOWSOC and the range of the torque request reqT . If LOWSOC SOC> , and (0, ]req elowT T∈ , execute step3A1. If LOWSOC SOC> , and ( , )req elow ehighT T T∈ , execute step3A2. If LOWSOC SOC> , and max[ , )req ehigh eT T T∈ , execute step3A3. If LOWSOC SOC> , and max max[ , )req e ehigh ISGT T T T∈ + , execute step3A4. If LOWSOC SOC> , and max max max[ , )req ehigh ISG e ISGT T T T T∈ + + , execute step3A5. If LOWSOC SOC< , and (0, ]req elowT T∈ , execute step3B1. If LOWSOC SOC< , and ( , )req elow ehighT T T∈ , execute step3B2. If LOWSOC SOC< , and max[ , )req ehigh eT T T∈ , execute step3B3. If LOWSOC SOC< , and max[ , )req eT T∈ +∞ , execute step3B4. where elowT , ehighT represent the lowest and highest curves of the high efficiency area for the engine respectively; maxeT , maxISGT represent the maximum torque of the engine and ISG motor respectively. Step3A1. Execute ISG drive mode, and return to execute step1. Then,

0ISG

e m

req h ISGT

T T

T J ω

= =

− =

(10)

where ISGω represents the angular speed of the ISG motor. Step3A2. Execute engine drive mode, and return to execute step1. Then,


( )

, 0

ISGe req q h ISG

ISG e

e eopt q e m

T T T J J

T T J T

ω

ω ω

ω

+ − = + = = + =

(11)

where eω represents the angular speed of the engine. Step3A3, Execute ISG-aided- parallel mode1, and return to execute step1. Then,

( )

, 0

ISGe req q h ISG

ISG e

e eopt q e m

T T T J J

T T J T

ω

ω ω

ω

+ − = + = = + =

(12)

Step3A4, Execute ISG-aided-parallel mode 2, and return to execute step1. Then,

( )

, 0

ISGe req q h ISG

ISG e

e ehigh q e m

T T T J J

T T J T

ω

ω ω

ω

+ − = + = = + =

(13)

Step3A4, Execute ISG-aided- parallel mode 3, and return to execute step1. Then,

max max

( )

min( , ), 0

ISGe req q h ISG

ISG e

ISG ISG ISG h ISG m

T T T J J

T T T J T

ω

ω ω

ω

+ − = + = = + =

(14)

Step3B1, Execute engine-drive-and-charge mode1, and return to execute step1. Then,

eopt

( )

( ) , 0

ISGe req q h e

ISG ch z ISG

e q h e m

T T T J J

T T J

T T J J T

ω

ω

ω

+ − = +

− =

= + + =

(15)


( )

( ) , 0

ISGe req q h e

ISG ch z ISG

e ehigh q h e m

T T T J J

T T J

T T J J T

ω

ω

ω

+ − = +

− =

= + + =

(16)


emax emax

( )

min , ( ) , 0

ISGe req q h e

ISG ch z ISG

e q h e m

T T T J J

T T J

T T T J J T

ω

ω

ω

+ − = +

− = = + + =

(17)

Step3B4, System warning, execute engine-drive mode1, and return to execute step1. Then,

emaxmin( , ) ( )

0, 0e req q h e

ISG m

T T T J JT T

ω − = +

= =

(18)

Step4. Execute 4WD mode. Step4A. Judging whether SOC is greater than its lowest work point LOWSOC , if yes, execute step 4B, otherwise execute step 4A1. Step4A1. System warning, execute engine-drive mode2, and return to execute step1. Then,

emaxmin( , ) ( )

0, 0e req q h e

ISG m

T T T J JT T

ω − = +

= =

(19)

Step4B. Judging the range of the torque request reqT , if max max max max[ , )req e ISG e mT T T T T∈ + + , execute step4B2. Step4B1. Execute rear-motor-aided-4WD mode, and return to execute step1. Then,

max max

( )

min( , ), 0

e m q h e r m

m m m r m ISG

T T J J J

T T T J T

ω ω

ω

+ = + + = + =

(20)

Step4B1. Execute all-hybrid-4WD mode, and return to execute step1. Then,

max max

max max

( )

min( , )

min( , )

e ISG m q h e r m

m m m r m

ISG ISG ISG h ISG

T T T J J J

T T T J

T T T J

ω ω

ω

ω

+ + = + + = + = +

(21)

5 Coordination Control during Mode Switches

The plug-in 4WD hybrid electric vehicle has many work modes, and during mode transitions, because of the different dynamic torque responses of the engine, ISG motor and rear motor, there may be some time when the total torque of the power components fluctuates or in the driveline, which seriously affects the comfort of the passengers and the power performance of the car [30]. To solve the problem, a coordination method based on “Preliminary torque distribution& clutch fuzzy-fuzzy PID control& engine dynamic torque lookup& dual motor compensation” during mode switches is put forward.


5.1 Clutch control The engagement process of the clutch can be classified into 3 steps, idle stroke stage, slipping stage, and synchronization stage. Because of the strong nonlinearity for the engagement process, it is very difficult to be expressed by math models, Du Bo, et al [31] adopted a fuzzy controller to regulate the oil pressure of the clutch, but fuzzy rules can’t adapt to different cycles. In order to have a more precise output of the oil pressure, a fuzzy-fuzzy PID method is used. The principle of the controller is presented in Fig. 5.

PreliminaryPressure

FuzzyController

Pressure Change

Rate FuzzyController

Fuzzy PIDController

Integral

+

++

+

Pb

P0

δPα’

|Δω|

α

P1

α’

P2

δP’

Fig. 5 The principle of the clutch controller

whereα represents the acceleration pedal travel; 'α represents the change rate of acceleration pedal

travel; ω∆ represents the difference of the angular speed for the active and passive plates of the clutch; Pδ represents the oil pressure increment in the clutch; 'Pδ represents the change rate of the original oil pressure; oP represents the original oil pressure in the clutch; bP represents the preliminary engagement oil pressure;

1P represents the preliminary output oil pressure;

2P represents the final output oil pressure. When the clutch is beginning to engage, the acceleration pedal travel α and its change rate can reflect the driver’s intentions. When α and 'α are big, the driver need the car to have a more powerful power performance. So the oil pressure increment in the clutch should be bigger. On the contrary, when α and 'α are small, the smoothness and the comfort are considered in the priority. So a small preliminary engagement oil pressure is needed to reduce jerk. In the slipping stage, change rate of acceleration pedal travel 'α is used to reflect the driver’s intentions and difference of the angular speed for active and passive plates of the clutch is used to control the jerk and friction work. When the driver pushes the pedal down very fast, he means to finish the mode switch very soon, so the change rate of the original oil pressure should be bigger. On the contrary, it should be slowed down. When difference of the angular speed for active and passive plates of the clutch is big, the change rate

of the original oil pressure should be slow in order to reduce jerk; and when it is small, the change rate of the original oil pressure should be fast to reduce the friction work. The procedures for the control of oil pressure of the clutch consist of three major parts: The first is the confirmation of the original oil pressure of the clutch. The inputs of the fuzzy controller are the acceleration pedal travel α and change rate of acceleration pedal travel 'α . The output of it is oil pressure increment Pδ . The preliminary engagement oil pressure bP is the sum of original oil pressure oP and oil pressure increment Pδ . Namely,

0bP P Pδ= + (22) The second is the confirmation of change rate of the oil pressure. The inputs of the fuzzy controller are the change rate of acceleration pedal travel 'α and the difference of the angular speed for active and passive plates of the clutch ω∆ . The output is the change rate of the original oil pressure 'Pδ . The preliminary output oil pressure 1P can be calculated by the following formula,

01 d dbP P P t tP PPδ δδ′ ′= + ⋅ = + + ⋅∫ ∫ (23)

The third is the adjustment of the final output oil pressure 2P using fuzzy PID controller. The inputs of the controller are the preliminary output oil pressure 1P and the final output oil pressure 2P . The final output oil pressure 2P can be calculated by the following formula,

2 1 2 1 2

1 2

( )( ) ( ) ( )d

d( )( )d

p i

d

P K P P Kt t P P t

P PK tt

= − + −

−+

∫ (24)

where Kp(t), Ki(t), Kd(t) represent the current proportion, integration and differentiation coefficients respectively, and they are adjusted real-time according to the cycles. For the domains and membership functions of the inputs and outputs variables are not discussed in detail here. The fuzzy rules of the oil pressure increment in the clutch and change rate of the original oil pressure are shown in table2 and table3 respectively [32].

Table2. Fuzzy rules of the oil pressure increment

△p α’

VS S MS M MB B VB

α

VS VS VS S S MS M M

S VS S S MS M M MB

MS S S MS M M MB MB


M S MS M M MB MB B

MB MS M M MB MB B B

B M M MB MB B B VB

VB M MB MB B B VB VB

Table2. Fuzzy rules of the change rate of the original oil pressure

△p’ |△ω|

VS S MS M MB B VB

α’

VS M MS S S VS VS VS

S MB M MS S S VS VS

MS B MB M MS S S VS

M B B MB M MS S S

MB VB B B MB M MS S

B VB VB B B MB M MS

VB VB VB VB B B MB M Note: VS、S、MS、M、MB、B、VB represent very small, small, medium small, medium, medium big, big, and very small respectively.

5.2 The coordination control strategy The mode switches can be classified into three kinds considering whether the engine is involved in: The first is EV to the modes in which engine propels the vehicle. This kind of switch involves the clutch engagement and engine dynamic torque

estimation; The second is the modes in which engine propels the vehicle to EV, which involves the disengagement of the clutches; The third is the switches among the modes in which the engine propels the vehicle, where it doesn’t involve the engage and disengage of the clutches. Of the three kinds of switches, the first is the most complicated. In the paper, only the mode switch between rear-motor-dive EV mode to ISG-aided-parallel mode, which is the first kind of switch, is taken as an example to introduce the detailed procedures of torque coordination control strategy. In the first kind of mode switch, the most significant part is the estimation of the engine dynamic torque. In order that the dynamic torque can be put into practice in real time. The engine throttle opening and its change rate and the speed are taken as 3 main factors to do experiments and the output torque of the engine is obtained. Then Matlab is used for interpolation of the experimental data to get the detailed relations between the output torque of the engine and its throttle opening, change rate and speed. According to which a three-dimensional lookup table is made and the engine dynamic torque can be obtained real-time by looking up the table. Parts of the data of the dynamic torque are shown in table4.

Table 2 Parts of the dynamic output torque of the engine

Engine speed(r/min)

Throttle opening (%)

Change rate of the throttle

opening

Dynamic torque(N.m)

Engine speed(r/min)

Throttle opening (%)

Change rate of the throttle

opening

Dynamic torque(N.m)

1000 25 0.5 24.7 4000 25 0.5 37.5

1000 50 1 51.3 4000 50 1 77.8

1000 75 1.5 77.4 4000 75 1.5 116.9

1000 100 2 107.5 4000 100 2 156.3

2000 25 0.5 31.6 5000 25 0.5 36.6

2000 50 1 67.1 5000 50 1 74.1

2000 75 1.5 100.8 5000 75 1.5 113.2

2000 100 2 135.1 5000 100 2 152.7

3000 25 0.5 35.9 6000 25 0.5 33.9

3000 50 1 73.6 6000 50 1 69.8

3000 75 1.5 111.7 6000 75 1.5 105.2

3000 100 2 149.4 6000 100 2 141.8

The procedures of the mode switch between rear-motor-dive EV mode and ISG-aided-parallel mode are as follows: First: Judging whether mode switches. If yes, the vehicle control unit (VCU) sends the order to start the engine. Then, clutch 1 engages with the appropriate rule according to the driver’s intentions. In the beginning of the engagement, the toque Tcl it transfers is less than the engine starting resistance torque Tf and engine doesn’t start. At this stage, the dynamic model can be expressed as below:

0

m req r m

ISG cl z ISG

e

T T J

T T JT

ω

ω

− =

− = =

(25)

Second: The toque Tcl clutch1 transfers is greater than the engine starting resistance torque Tf and engine starts to rotate. Before its speed arrives at the ignition speed, the engine works as a load, and the resistance torque is Tf. At this stage, the dynamic model can be expressed as below:


m req r m

ISG cl z ISG

f cl q e

T T J

T T J

T T J

ω

ω

ω

− =

− =

− =

(26)

Third: The engine speed arrives at the ignition speed, the engine is ignited. And then, clutch 2 engages with the appropriate rule according to the driver’s intentions. VCU confirms the best CVT ratio in accordance with the current velocity of the vehicle. And then the engine optimal dynamic torque is obtained by looking up the three-dimensional table, and ISG motor adjusts engine’s load and compensate part of the dynamic torque. Moreover, the torque error of the front axle is compensated by the rear axle. At this stage, the dynamic model can be expressed as below:

'

'

( )ISGe m req q h ISG r m

ISG e

e eopt q e

ISG req e h ISG

T T T T J J J

T T J

T T T J

ω ω

ω ω

ω

ω

+ + − = + +

= = + = − +

(27)

Where T'eopt is the optimal dynamic torque looked up in the three-dimensional table.

Fourth: If the torque request is met by the front axle, the mode switch ends. And the power components work at the point of the preliminarily distributed torque.

6 Hardware-in-loop Bench Test In order to validate the effectiveness of the torque coordination control strategy, it is compiled to executable code and downloaded to dSPACE, which acts as a HCU. The test cycle is set to 10*NEDC, which consists of 10 NEDC cycles. And the initial SOC of the battery is 0.6. The test bench of the hybrid system is shown in Fig.6 and the experimental results are as follows.

ECU EngineISG

CVT Final Drive

Brake

InertialFlywheel

Eddy Current Dynamometer

Hydraulic Controller

Control Desk

ISG ControllerBMS Power

Cell

Clutch1 Clutch2

Sensor 1 Sensor 2

Rear Motor

Final Drive

Sensor 3

InertialFlywheel

Eddy Current Dynamometer

Brake

MCU

CVT Controller

HCU

CAN

Fig.6 Test bench of the hybrid system

6.1 Results of energy management coordination control

The results of energy management coordination control are illustrated in Fig.7.

0 2000 4000 6000 8000 10000 120000.9

0.95

1

1.05

(a)Time(sec)

Torq

ue r

eque

st

coef

ficie

nt K

0 2000 4000 6000 8000 10000 120000.2

0.4

(b)Time(sec)

Bat

tery

SO

C /(

%)

0 2000 4000 6000 8000 10000 120000

50

100

150

(c)Time(sec)

Eng

ine

torq

ueTq

e/(N

.m)

0 2000 4000 6000 8000 10000 12000-100

0

100

(d)Time(sec)

Rea

r m

otor

To

rque

Tqm

/(N.m

)

0 2000 4000 6000 8000 10000 12000-100

-50

0

50

(e)Time(sec)

ISG

mot

or T

orqu

eTI

SG

/(N.m

)

0 2000 4000 6000 8000 10000 120000

2

4

(f)Time(sec)

Fuel

con

sum

ptio

n /(

L)

without fuzzy PIDwith fuzzy PID

Fig.7 Results of energy management coordination control

From Fig7 (a), it is clearly seen that the range of the torque identification coefficient is mainly between 0.92-0.94, which indicates that it is small, and the hybrid electric vehicle is mainly driven by the rear motor. Moreover, in the EUDC cycle of the NEDC, the range of the torque identification coefficient is mainly between 0.95-1.02, which indicates that it is medium, and the vehicle is driven by the front axle. From the torque request, SOC of the battery and the outputs of the power components, as shown in

Fig. 7(b)-(e), it is clearly seen that the vehicle is in charge-depleting mode in the first 3 NEDC cycles. In which the rear motor drives the vehicle and it outputs the required torque. The engine and ISG motor output positive torque only in EUDC cycle, which proves that the vehicle is in the ISG-aided-parallel mode. In the following 7 NEDC cycles, the vehicle is in the charge-sustaining mode, and the vehicle is still mainly driven by the rear motor. In the EUDC cycle, the engine and ISG motor output positive torque, and in ECE cycle, the


engine outputs positive torque, ISG motor outputs negative torque, and rear motor outputs positive torque. So, it is clear that in the ECE cycles of the last 7 NEDC cycles, the vehicle works at Series mode, while in the EUDC cycles, the vehicle works at ISG-aided-parallel mode. From Fig.7 (f), it is clearly seen that the fuel consumptions for 100km with and without torque identification are 3.12L and 3.43L, which proves that the fuel consumption is reduced by 9.04% and the use of torque identification can improve fuel economy.

6.2 Results of coordination control during EV to ISG-aided-parallel mode

In the paper, the jerk, longitudinal acceleration, and output of the power components are used to evaluate the effectiveness of the torque coordination control and the filtered results are illustrated in Fig.8.

13 13.5 14 14.5 15 15.5 16-10

0

10

(a)Time(sec)

Jerk

dur

ing

mod

e

tran

sitio

n(m

/s3 )

without coordinationwith coordination

13 13.5 14 14.5 15 15.5 160

20

40

60

(b)Time(sec)

Torq

ue o

utpu

t with

out

coor

dina

tion(

N.m

)

motor torqueengine torqueISGtorque

13 13.5 14 14.5 15 15.5 160

20

40

60

(c)Time(sec)

Torq

ue o

utpu

t with

coo

rdin

atio

n(N

.m)

motor torqueengine torqueISG torque

Fig.8 Results of coordination control during mode

switch

From Fig.8 (a), it can be seen that the absolute value of the jerk without torque coordination control is11.3 m/s3, while with it the maximum absolute value is 6.7 m/s3, which is reduced by 40.7% and the comfort of the driver is greatly reduced. From Fig.8(b)-(c), it is clearly seen that without coordination control, the torque of the rear motor drops to zero quickly and the torque of the ISG motor synchronize with the engine torque. So the motors don’t work as compensators. While with coordination control, the torque of the rear motor drops to zero at a certain pace, and the change rate of the torque for ISG motor is faster and it arises to a certain peak before dropping to

its stable value, which proves that the 2 motors works as compensators in the mode switch between rear-drive-EV mode to ISG-aided-parallel mode. Therefore, it is clear that the adoption of torque coordination control strategy proposed in the paper can realize very good control effect.

7 Conclusions: 1) The diver model based on fuzzy PID can

reflect the driver’s intentions and the fuel consumption is reduced by 9.04%.

2) With coordination control of energy management, the vehicle works well in the stable state, which is validated by the output of the power components.

3) With coordination control during mode switches, the maximum absolute value of jerk is reduced by 40.7%.

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Authors

Lijun Qian received his Ph.D. degree in School of Mechanical and Automotive Engineering, Hefei University of Technology, Hefei, China, in 2004. Since 2004, he has been a professor in Hefei University of Technology. His research interests include vehicle modern design theory and method, vehicle safety technology, and electrical vehicle technology.

Lihong Qiu received his B.E. degree from Hefei University of Technology, Hefei, China, in 2013. He is now pursuing his Ph.D. degree in School of Mechanical and Automotive Engineering, Hefei University of Technology. His research interests include the energy management strategy for plug-in 4WD hybrid electric vehicle and the dynamic control for the HEV.

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