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

摘要/Abstract

摘要：

针对复杂行驶工况易导致混合动力汽车动力电池使用条件恶化、增加车辆全寿命周期成本的问题，以一款行星混联式混合动力汽车为研究对象，从能量管理优化角度主动延长电池寿命。基于历史数据构建了实车代表性工况，采用动态规划算法求解以整车综合燃油消耗和电池寿命衰减最小的多目标优化问题，保证系统性能全局最优。由于动态规划算法具有工况局限性且运算量大，因而基于全局优化结果训练神经网络控制器来实现能量管理控制。仿真结果表明：与以油耗为单目标优化相比，多目标优化可使电池寿命衰减减少43.28%，油耗仅增加1.22%，在减缓电池寿命衰减的同时兼顾了燃油经济性，基于神经网络的控制策略可实现主动适应驾驶员日常行驶工况的优化控制，具有良好的应用前景。

关键词: 车辆工程, 行驶工况, 动态规划, 电池寿命, 神经网络

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

中图分类号:

U469.7

宋大凤,杨丽丽,曾小华,王星琦,梁伟智,杨南南. 基于行驶工况合成的混合动力汽车电池寿命优化[J]. 吉林大学学报(工学版), 2021, 51(3): 781-791.

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.

图/表 20

图1

表1

图2

图3

表2

图4

表3

合成工况下的仿真结果"

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

表3

图5

图6

图7

图8

图9

图10

图11

图12

表4

电池高倍率和超高倍率充放电工作点比例"

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

表4

图13

图14

表5

动态规划优化和神经网络模型预测结果对比"

$μ = 0.3$	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

表5

图15

参考文献 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

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