吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (3): 781-791.doi: 10.13229/j.cnki.jdxbgxb20200077

• 车辆工程·机械工程 •    下一篇

基于行驶工况合成的混合动力汽车电池寿命优化

宋大凤(),杨丽丽,曾小华(),王星琦,梁伟智,杨南南   

  1. 吉林大学 汽车仿真与控制国家重点实验室,长春 130022
  • 收稿日期:2020-02-14 出版日期:2021-05-01 发布日期:2021-05-07
  • 通讯作者: 曾小华 E-mail:songdf@126.com;zeng.xiaohua@126.com
  • 作者简介:宋大凤(1977-),女,教授,博士生导师. 研究方向:汽车电控. E-mail:songdf@126.com
  • 基金资助:
    国家重点研发计划项目(2018YFB0105900)

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

Da-feng SONG(),Li-li YANG,Xiao-hua ZENG(),Xing-qi WANG,Wei-zhi LIANG,Nan-nan YANG   

  1. State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
  • Received:2020-02-14 Online:2021-05-01 Published:2021-05-07
  • Contact: Xiao-hua ZENG E-mail:songdf@126.com;zeng.xiaohua@126.com

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

图1

行星混合动力系统构型"

表1

整车参数"

参数数值
整车质量/kg16550
车轮半径/m0.505
迎风面积/m27.8
发动机峰值功率147 kW@2300 r/min
发电机MG1峰值功率/kW100
主电机MG2峰值功率/kW200
动力电池额定电压/V600
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

图2

构建合成工况的算法结构"

图3

合成工况"

表2

原始工况和合成工况的统计特征对比"

统计特征原始工况合成工况
平均车速/(km?h-1)17.617.3
平均行驶车速/(km?h-1)21.621.2
车速标准差/(km?h-1)11.111.4
最高车速/(km?h-1)56.055.0
平均加速度/(m?s-2)0.350.35
平均减速度/(m?s-2)-0.43-0.43
怠速比例/%18.218.5
加速比例/%44.543.8
制动比例/%35.435.2

图4

车速-加速度联合概率密度"

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/%

Aheff

/(A·h)

Aheff

变化/%

017.10-53.79-
0.0517.150.3236.3632.41
0.117.180.5033.2938.10
0.217.240.8331.4941.45
0.317.311.2230.5143.28
0.517.411.8329.7144.76
0.717.502.3629.4645.22
117.582.7929.4445.26

图5

动态规划优化结果"

图6

有效电量对比"

图7

燃油消耗量对比"

图8

电池充放电倍率对比"

图9

电池SOC对比"

图10

整车需求功率和发动机功率"

图11

电池充放电倍率、功率"

图12

电池高倍率和超高倍率充放电工作点分布"

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/%
020.644.34
0.312.810.2

图13

基于神经网络的能量管理策略控制框架"

图14

动态规划优化和神经网络预测的电池功率"

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)17.2617.47
Aheff/(A·h)59.7158.54
SOC初始值/终值0.8/0.80.8/0.7953
运算时间/s4500448

图15

动态规划优化和神经网络预测的发动机工作点分布对比"

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