World Electric Vehicle Journal Vol. 4 - ISSN 2032-6653 - © 2010 WEVA
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EVS25 Shenzhen, China, Nov 5-9, 2010
Development for Hybrid MPV Control Strategy 1
1
2
3
1
1
Hongtao Peng , Zheng Li , Yu Zhu , Pingxing Xu , Yuehong Shu , Yong Shi , Yuan Liang 1
1
Dongfeng Motor Corporation Technical Center, Wuhan, China,
[email protected] 2
Shanghai Jiao Tong University, Shanghai, China 3
Dongfeng Motor Corporation, Wuhan, China
Abstract Hybrid MPV control strategy is researched by building simulation model under Matlab/Simulink/Stateflow environment, and auto-generated code is downloaded to vehicle control unit. With this approach, proved by experiment, the development of hybrid MPV control strategy is of high efficiency and reliability, with the performance met the design requirements, and the portability and maintainability of system improved. Keywords: Hybrid Electric Vehicle (HEV), Control strategy, Simulation Model ,Automatic Generation of Code
1 Introduction In the 21st century, as the energy resources shortage and environment protection have become the major challenge for the world, it is imperative to alter the energy and power system for vehicle industry, the electric vehicle has become the essential choice for such alteration. The project of development of LZ6460Q8HEV (Hybrid MPV) comes from the project of national 11th five-year-plan and national 863 plan. Using a conventional two wheel drive MPV, adding electric motors and battery, a four wheel drive function hybrid MPV is developed, it shows good off-road capacity, power performance, fuel economy and good emission performance. According to conventional power train configuration and 3-D model, using a BSG replacing the conventional starter and generator, using an electric motor assisting to drive the vehicle and regenerate the brake power in the rear wheels, a four wheel drive function hybrid MPV layout is realized, as shown in Figure1.
Figure 1: The general arrangement of Hybrid MPV
2 Control strategy and modeling simulation 2.1 Modeling The simulation model of Hybrid MPV is established via matlab /simulilnk /stateflow. First, we can calculate the power performance and
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World Electric Vehicle Journal Vol. 4 - ISSN 2032-6653 - © 2010 WEVA
the economy performance of the conventional vehicle, then we can adjust and validate the model through the comparison between the simulation results and real experimental results. Afterward, we can use a validated vehicle model to simulate Hybrid MPV, such as matching motors and battery, researching the energy distribution strategy & regeneration strategy of braking energy, optimizing the work area of engine, motor, and battery, etc. Finally, an optimal control strategy is designed. Simulation model includes driver’s intention submodule, energy distribution strategy submodule, vehicle dynamics submodule, result submodule and so on. Figure 2 shows the model.
Figure 2: Simulation model of Hybrid MPV
2.2 The operation mode of Hybrid MPV According to a typical driving cycle of Hybrid MPV, there are several working modes, as shown in Figure3. 1) Starting: Only the pure engine is used for start-up and low speeds. 2) Normal Driving: While cruising, the engine and motor both drive the wheels,power allocation is controlled to maximize efficiency of engine. As necessary, the motor also recharges the battery from surplus engine power. 3) Acceleration: While accelerating or climbing, the engine and motor both drive the wheels. 4) Deceleration: While decelerating or braking, the “regenerative braking system” recovers kinetic energy as electrical energy, which is stored in the high-performance battery. 5) Stopping: At stops, the engine shuts off automatically and the BSG stands ready to power up the vehicle.
Figure 3: The operation mode of Hybrid MPV
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2.3 Control strategy In the design of the control strategy, different structure hybrid vehicles need different control strategies with reasonable control and adjustment of energy flow distribution. Therefore, according to different optimization targets, we should select different control strategies of energy management system to achieve optimal design goal under limited conditions. How to optimize the control strategy is the key technique to reduce fuel consumption and emission after selecting all parts of HEV. On the premise that the requirements of vehicles basic performances(such as power & economy performance) and cost should be met, considering the special features of each part and different operation modes, the energy should be reasonably distributed between engine and motor under control strategy, which makes the whole vehicle system efficient higher, fuel consumption lower and emission lower, and at the same time keep the driveability good. We adopts the instantaneous optimal control strategy based on the optimal working curve of engine in the project. For the Hybrid MPV under a particular operating point, we optimize the entire power system for optimization goal to get the best instantaneous operating point, then redistribute various state variables dynamically based on instantaneous optimal system operating point. According to the economy and emission characteristics of the engine, an appropriate objective function is established through the optimal control theory. To minimize the objective function,we can achieve good power performance, fuel economy and emission performance. min( f ) = min{ω 1 (
a b c ) + ω 2 ( ) + ω 3 ( )} a0 b0 c0
(1)
In Equation (1), f is the objective function; a is the fuel consumption (L/100km); b is actual emission (g/100km); c is acceleration time (s);
a0 , b0 and c0 respectively are the aim values;
ω1 , ω 2 and ω 3 are the corresponding weight
coefficients. Weight coefficient can be adjusted to change the degree of influence of each parameter. When designing control strategy, we consider the engine’s, motor’s and battery’s instantaneous efficiency. Combining with the actual running state, Such as the engine’s, motor’s, battery’s temperature and the braking energy recovery, etc, the best combination of energy between engine and motor is obtained. According to the control
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World Electric Vehicle Journal Vol. 4 - ISSN 2032-6653 - © 2010 WEVA
target, then the optimal engine and motor operating points are determined. Our goal is to make the optimal efficiency of the system.
2.4 Simulation Using the established simulation model is to get the results of power & economy performance, so we can compare different control strategies and different control parameters and get a better choice of strategy and key parameters. The following, we choose a better control strategy and simulate the model, then analyze the results. Figure 4 shows the acceleration time from 0 to 100km/h, Hybrid MPV’s acceleration time is significantly less than the conventional vehicle’s, the power performance of Hybrid MPV is better than the conventional vehicle’s. 160
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engine efficiency map during the vehicle running in one NEDC driving cycle. The optimal engine operating curve is the minimum engine fuel consumption curve which connects the minimum engine fuel consumption speed-torque operating points at different engine speeds. The engine has better operation at the more efficient region in the engine efficiency map. Due to the adjustment of the engine’s operating point, Hybrid MPV’s engine is more efficient than the conventional vehicle’s, as shown in the figure. The motor operates at the efficient region in the motor efficiency map in the NEDC driving cycle, as shown in Figure 6.
Conventional MPV Hybrid MPV
140
120
Speed(km/h)
100
80
60
40
20
0 0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
Figure 6. Motor operating points
30
Time(s)
Figure 4. Acceleration time
The high efficiency region of battery for the Hybrid MPV is between 50% and 60%, the battery works in this region in the NEDC driving cycle, and SOC balances at 55%, as shown in Figure 7. 0.6
SOC Vehicle Speed
0.59
140
120 0.58
0.56 SOC
80
0.55 60
0.54 0.53
Speed(km/h)
100
0.57
40
0.52 20 0.51 0.5 0
Figure 5. Engine operating points
200
400
600 Time(s)
800
1000
0 1200
Figure 7. The curve of SOC
Figure 5 shows the engine operating points in the
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World Electric Vehicle Journal Vol. 4 - ISSN 2032-6653 - © 2010 WEVA
Table 1: Hybrid MPV characteristics
3 Code implementation Hand-written code and automatic generation of code are adopted, and hand-written C language is used in controller driver program which includes input and output signal processing, sensor signal processing, port initialization such as I/O, A/D, PWM, CAN and flash operation. The control strategy simulation model is generated C code under the Real-Time Workshop Embedded Coder environment of MATLAB, then the generated code is integrated into the driver program, compiling the whole code, and downloaded to the HCU. The model of automatic code generation is shown in Figure 8. 1 fc_first _start_flag
fc _first _start_flag
2 mpv _fc _speed _test
m pv_fc _speed_test
3 receive _ess_voltage
receive _ess_voltage
4 di _cur_gear
mc_drive _torque
5 mc_drive _torque
fc _drive _torque
6 fc_drive _torque
Parameter
Gross Mass（kg）
Value
Power performance
2370
Economy performance
2150
Engine power（kW）
87kW/5500rpm
Motor power（kW）
22kW/1400rpm
Battery
8Ah
Gear ratios
Gear 1
3.968
Gear 2
2.137
Gear 3
1.36
Gear 4
1
Gear 5
0.857
Gear R
3.579
Front axle
4.875
Rear axle
4.2727
di_cur_gear
5 clutch _pedal _state
mc_driving _charge_torque
clutch_pedal_ state
7 mc _driving _charge _torque
di_key _start
Final drive
6 di _key_start stop_state_hold_cont
m pv_vehicle _speed_test
7 mpv _vehicle _speed _test
1 stop_state_hold _cont
receive _ess_soc
8 receive _ess_soc 9 receive _bsg_speed
receive _bsg_speed
10 receive _ess_temp
receive _ess_temp
di_R_gear
11 di _R_gear 12 send_ad _acc_pedal _degree
do_mc_state _ctrl
2 do _mc _state_ctrl
send_mc_torque
3 send _mc_torque
send_ad_acc_pedal_degree
da_out_value
receive _m c_speed
13 receive _mc_speed
4 da _out _value
send_ad_brake_pedal_degree
14 send_ad _brake_pedal _degree 15 send_ad _clutch _degree
send_ad_clutch_degree
fc _first _start_flag 1
8 fc_first _start_flag 1
do_bsg_state_ctrl 2
9 do _bsg_state_ctrl2
receive _ess_charge_current_total
16 receive _ess_charge _current _total
receive _m c_model_temp
17 receive _mc_model _temp 18 do _bsg_state_ctrl
Wheel radius (m) Coefficient of Rolling Resistance (a*v^2+b*v+c)
0.313 a
0.098
b
-2.1198
c
276.3231
do_bsg_state _ctrl
bsg_start _timer_cont_new2
do_fc _stop_ctrl
19 do _fc _stop_ctrl 20 stop_state_hold _cont 1
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10 bsg_start_timer _cont _new 2
stop_state _hold_cont1
Memory 2 bsg_continue_start_err_cnt
do_fc _stop_ctrl 3
Table 2: The simulation and experiment result
11 bsg_continue _start_err_cnt
Memory 1 do_bsg_state _ctrl1
21 do _bsg_state_ctrl1
22 bsg_start_timer _cont _new 1 23 bsg_continue _start_err_cnt 1 24 fc _running 1 25 do _mc_state_ctrl1 26 send_mc_torque 1
Memory 3
Memory 4
bsg_start _tim er_cont_new1
fc _running
12 fc_running
bsg_continue_start_err_cnt1
send_bsg_charge_power
fc _running1
Memory 5
13 send _bsg_charge _power
do_fc _stop_ctrl 2
send_m c_torque1
Memory 7
Test Method
Tradition al Vehicle
Hybrid
simulation
130
142
experiment
>120
>120
0-100km/h simulation acceleratio n time（s） experiment
22.6
15.8
23.7
16.8
Test Project
do_mc_state _ctrl 1
Memory 6 14 do _fc_stop_ctrl2
Subsystem1 Memory 8
4 Vehicles experiment We modify and improve the control strategy through the road test, after the road test, the performance experiment is conducted in National Quality Control & Inspection Center for Automobiles (Xiangfan). The overall parameter of Hybrid MPV is explained in Table 1 , the simulation and experiment result is shown in Table 2 respectively. There is little disparity between the simulation result and test, which demonstrates that the strategy in simulation model has already taken effect and verifies the correctness and validity of the simulation model in turn. The Hybrid MPV experiment results demonstrate 23% fuel economy improvement contrasting conventional vehicle, and the power performance and economy performance have reached the 863 contract requirements.
Power Performance
Figure 8. The model of automatic code generation
Max speed （km/h）
Grade ability
simulation
41
44
(%)
experiment
>30
40
simulation
12.5
9.8
experiment
12.9
9.9
Fuel Consumption （L/100km)
5 Conclusion Hybird MPV control strategy is researched by building simulation model under Matlab/Simulink/Stateflow environment, and automatic code generation of simulation model is successfully applied into the research and development of the Hybird MPV. With this
EVS25 World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium
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World Electric Vehicle Journal Vol. 4 - ISSN 2032-6653 - © 2010 WEVA
approach, proved by experiment, the development of hybrid MPV control strategy is of high efficiency and reliability, with the performance met the design requirements and the portability and maintainability of system improved, meanwhile the development cost is greatly reduced, and the development cycle is shortened.
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Yu Zhu is professor engineer, his research interests focus on control strategies and project management in HEVs.
References [1]
Y.S. WONG, K.T. Chau, C.C. Chan, T.W. Ching, Battery Evaluation and Sizing for Plug-in Hybrid Electric Vehicles, EVS22, 2006, pp: 1858-1867.
[2]
PU Jin-huan, YIN Chen-liang, ZHANG Jian-wu, Fuel Optimal Control of Parallel Hybrid Electric Vehicles, Journal of Shanghai Jiaotong University,2006, pp: 947-957.
[3]
Miaohua Huang, Houyu Yu, Optimal Energy Management Strategy for Parallel Hybrid Electric Vehicles, EVS22, 2006, pp: 1442-1448. Liu Xiaokang, ZHU Yu,Chang Yunping, PENG Hong-tao, Model Based Design for Development of Battery Management System,SAE paper C2008A621, pp: 1551-1554.
[4]
[5]
Pingxing Xu is professor engineer, his research interests focus on mechanical structure design and project management in HEVs.
Yuehong Shu is an engineer, his research interests focus on Software design and test in HEVs.
PENG Hong-tao,LI Zheng, ZHU Yu,LIANG Yuan,WANG Bin, The Present Situation and Suggestions of New Energy Vehicles in China, Automobile Science & Technology,2009, pp: 1-4.
Authors Hongtao Peng received his Bachelor's degree in computer science and technology in 2003 and the Master’s degree in automobile engineering in 2006, from Wuhan University of Technology, China. He is mainly engaged in research in the field of modeling, simulation and optimization of power systems, control strategies and software development for hybrid electric vehicle and electric vehicle.
Yong Shi is a senior engineer, his research interests focus on mechanical structure design and test in HEVs.
Yuan Liang received her MS from the Department of Mechanical Engineering, Huazhong University of Science and Technology in 2007. Her research interests focus on design and control of HEVs.
Zheng Li is a professor engineer, her research interests focus on control strategies, simulation and test in HEVs.
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