基于行驶工况合成的混合动力汽车电池寿命优化

doi:10.13229/j.cnki.jdxbgxb20200077

Abstract

Abstract:

To solve the problems that the use conditions of hybrid electric vehicle power batteries deteriorate and the full-life-cycle cost of vehicle increases due to complex driving cycle， a planetary hybrid electric vehicle was taken as the research object to proactively extend the battery life from the perspective of energy management optimization. Based on the historical driving data， a representative real vehicle driving cycle was built. The dynamic programming algorithm was used to solve the multi-objective optimization problem with the minimum overall fuel consumption and battery life attenuation to ensure the global optimal system performance. Due to the problem that dynamic programming algorithm has limitation of driving cycle and demands large amount of calculation， the neural network controller was trained for realizing energy management control based on the global optimization results. Simulation results show that compared with the optimization with a single goal of fuel consumption， multi-objective optimization can reduce battery life attenuation by 43.28% and increase fuel consumption by only 1.22%， which slows battery life attenuation while taking into account fuel economy. The control strategy based on neural network achieves optimized control that actively adapts to the daily driving cycle of the driver， which has a good application prospect.

Key words: vehicle engineering, driving cycle, dynamic programming, battery life, neural network

CLC Number:

U469.7

Da-feng SONG,Li-li YANG,Xiao-hua ZENG,Xing-qi WANG,Wei-zhi LIANG,Nan-nan YANG. Battery life optimization of hybrid electric vehicle based on driving cycle construction[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 781-791.

Figures/Tables 20

Fig.1

Table 1

Fig.2

Fig.3

Table 2

Fig.4

Table 3

Simulation results under syntheticdriving cycle"

权重系数 $μ$	油耗 /[L·(100 km)^-1]	油耗变化/%	$A h e f f$ /(A·h)	$A h e f f$ 变化/%
0	17.10	-	53.79	-
0.05	17.15	0.32	36.36	32.41
0.1	17.18	0.50	33.29	38.10
0.2	17.24	0.83	31.49	41.45
0.3	17.31	1.22	30.51	43.28
0.5	17.41	1.83	29.71	44.76
0.7	17.50	2.36	29.46	45.22
1	17.58	2.79	29.44	45.26

Table 3

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Table 4

Proportions of battery charge-dischargeoperating points at high-rate andultra-high-rate"

权重系数 $μ$	高倍率充放电比例/%	超高倍率充放电比例/%
0	20.64	4.34
0.3	12.81	0.2

Table 4

Fig.13

Fig.14

Table 5

Comparison of results between dynamicprogramming optimization andneural network prediction"

$μ = 0.3$	动态规划算法	神经网络预测
油耗/(L/100 km)	17.26	17.47
$A h e f f$ /(A·h)	59.71	58.54
SOC初始值/终值	0.8/0.8	0.8/0.7953
运算时间/s	4500	448

Table 5

Fig.15

References 21

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参数	数值
整车质量/kg	16550
车轮半径/m	0.505
迎风面积/m²	7.8
发动机峰值功率	147 kW@2300 r/min
发电机MG1峰值功率/kW	100
主电机MG2峰值功率/kW	200
动力电池额定电压/V	600
动力电池能量/(kW·h)	10.8
前行星排特征参数	2.63 2.11
后行星排特征参数	2.63 2.11

统计特征	原始工况	合成工况
平均车速/(km?h^-1)	17.6	17.3
平均行驶车速/(km?h^-1)	21.6	21.2
车速标准差/(km?h^-1)	11.1	11.4
最高车速/(km?h^-1)	56.0	55.0
平均加速度/(m?s^-2)	0.35	0.35
平均减速度/(m?s^-2)	-0.43	-0.43
怠速比例/%	18.2	18.5
加速比例/%	44.5	43.8
制动比例/%	35.4	35.2

Battery life optimization of hybrid electric vehicle based on driving cycle construction

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