Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1636-1644.doi: 10.13229/j.cnki.jdxbgxb20170873

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Adaptive energy management strategy for extended range electric vehicle

XI Li-he1(),ZHANG Xin1(),SUN Chuan-yang1,WANG Ze-xing2,JIANG Tao1   

  1. 1. Beijing Key Laboratory of Powertrain for New Energy Vehicle, Beijing Jiaotong University, Beijing 100044,China
    2. Engineering Research Institute, Beijing Electric Vehicle, Beijing 102606,China
  • Received:2017-08-21 Online:2018-11-20 Published:2018-12-11

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Abstract:

The control objective for Extended Range Electric Vehicle (EREV) is to control battery State of Charge (SOC) to reach the lower bound as the vehicle arriving at charging station. First, the minimum fuel consumption and SOC varying along target curve were added in cost-function, then, Dynamic Programming (DP) algorithm was adopted to solve the optimization problem. To achieve real-time control performance, a neural network framework was applied to train the optimal results and generate control model. Simulation results show that the generated model has better learning performance that the difference error is within 3%. Since the control model is only fit for special driving cycle and driving distance, an adaptive energy management controller was proposed. It consists driving pattern recognition module, control model library and control model selection module, etc. Hardware-in-Loop test was conducted to validate the controller performance. The results show that the proposed controller can control battery SOC just dropped to lower bound at the end of driving cycle, and it improves fuel economy for 9.2% compared to CD/CS algorithm under HWFET driving cycle.

Key words: vehicle engineering, extended range electric vehicle, dynamic programming, neural network, energy management strategy

CLC Number: 

  • U469.72

Fig.1

EREV configuration"

Table 1

EREV components parameters"

部件 参数 数值
发动机 排量/L 0.9
最大功率/kW 52
最大扭矩/(N·m) 90
发电机 最大功率/kW 53
最大扭矩/(N·m) 155
驱动电机 最大功率/kW 130
最大扭矩/(N·m) 480
动力电池 电容量/(A·h) 37
电压/V 360
其他 主减速器传动比 7.793
整备质量/kg 1400
迎风面积/m2 2.9
轮胎滚动半径/m 0.298
传动系统效率/% 92

Table 2

Parameters value for vehicle model"

项目 数值 单位
f 0.015 -
ηt 92 %
α 0 %
δ 1.0425 -
CD 0.33 -
A 2.9 m2

Fig.2

Generating power vs extender fuel rate look-up table"

Fig.3

Rint battery model"

Fig.4

Neural network training model"

Fig.5

Simulation results comparison for DP and Neural Network model under 120 km NEDC cycle"

Fig.6

Simulation results under various driving conditions"

Fig.7

Flow chart of selection module in control model"

Fig.8

Schematic of hardware in loop platform"

Fig.9

Physical image of hardware in loop platform"

Fig.10

HIL test results (LA92 Cycle)"

Table 3

Fuel economy comparison for driving distance 200 km (LA92 cycle)"

类别 行驶时间
/h		 目标行驶
里程/km		 燃油消耗
/L		 SOC%
初始值 结束值
动态规划 5.19 205.4 7.42 95 29.3
硬件在环 5.19 205.4 7.95 95 30.02

Fig.11

Battery SOC variation for three control algorithms under atypical driving distance(HWFET cycle)"

Table 4

Fuel economy comparison for driving distance 165 km (HWFET cycle)"

类别 行驶时间/h 目标行驶
里程/km		 燃油消耗/L SOC% 等效燃油
消耗/L		 节油率/%
初始值 结束值
CD/CS 4.92 165 5.22 95 29.5 5.23 -
NN2,,1+CD/CS 4.92 165 4.87 95 30.2 4.87 6.9
NN2,1+ NN2,2 4.92 165 4.77 95 31.5 4.75 9.2
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