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基于Pareto的电池容量衰退权衡优化控制策略

林歆悠 叶常青 苏炼

林歆悠, 叶常青, 苏炼. 基于Pareto的电池容量衰退权衡优化控制策略[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.03.01.005
引用本文: 林歆悠, 叶常青, 苏炼. 基于Pareto的电池容量衰退权衡优化控制策略[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.03.01.005
LIN Xin-you, YE Chang-qing, SU Lian. Pareto-based optimal control strategy for battery capacity decline[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.03.01.005
Citation: LIN Xin-you, YE Chang-qing, SU Lian. Pareto-based optimal control strategy for battery capacity decline[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.03.01.005

基于Pareto的电池容量衰退权衡优化控制策略

doi: 10.13374/j.issn2095-9389.2021.03.01.005
基金项目: 福建省自然科学基金资助项目(2020J01449);国家自然科学基金资助项目(51505086);安徽工程大学检测技术与节能装置安徽省重点实验室开放研究基金资助项目(JCKJ2021A04)
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    通讯作者:

    E-mail: linxyfzu@126.com

  • 中图分类号: U461

Pareto-based optimal control strategy for battery capacity decline

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  • 摘要: 由于插电式混合动力汽车电池可以通过电网获取比较廉价的电量,传统的控制策略只考虑充分利用电池电量,但忽略了过度使用电池,会加快动力电池容量的衰退。因此,如何权衡充分利用电池电量与抑制电池容量衰退是新的研究重点。基于电池的半经验衰退模型,引入电池利用程度因子,建立权衡电池容量衰退的能量管理策略。通过Pareto非劣目标域选取合适的权重因子,将多目标优化问题转化为单目标问题,采用动态规划算法获得权重系数全局最优解,通过权衡不同权重下的油耗和电池容量衰退程度选择最优权重系数。在燃油消耗相当的情况下,当权重系数为0.9时,可有效抑制电池寿命的衰减速度。最后,通过在线等效油耗最小策略仿真与在同一权重下的动态规划解进行比较来验证其有效性。

     

  • 图  1  电池循环次数与放电深度的关系

    Figure  1.  Relationship between the number of battery cycles and the depth of discharge

    图  2  不同放电倍率下的电池衰退率

    Figure  2.  Battery decay rate at different discharge rates

    图  3  不同温度下的锂电池寿命曲线

    Figure  3.  Lithium-ion battery life curve at different temperatures

    图  4  不同充电截止电压下的电池容量衰退

    Figure  4.  Battery capacity degradation at different charge cutoff voltages

    图  5  电池SOC、温度和严重程度系数关系图

    Figure  5.  Chart of battery SOC, temperature, and severity coefficient

    图  6  不同温度下的严重程度系数关系图。(a)15 ℃;(b)30 ℃;(c)45 ℃;(d)60 ℃

    Figure  6.  Relationship diagram of severity coefficients at different temperatures:(a) 15 ℃;(b) 30 ℃;(c) 45 ℃;(d) 60 ℃

    图  7  考虑电池容量衰退的权衡控制策略DP求解流程图

    Figure  7.  Solution flow chart of the trade-off control strategy DP considering battery capacity decline

    图  8  7个US06工况下不同权重时的DP解

    Figure  8.  DP solutions of seven US06 operating conditions with different weights

    图  9  不同权重下的SOC曲线。(a)车速示意图;(b)SOC变化曲线

    Figure  9.  SOC curves under different weights: (a) speed diagram; (b) SOC change curve

    图  10  不同权重下的仿真结果。(a)充放电倍率;(b)严重程度系数

    Figure  10.  Simulation results under different weights: (a) charge and discharge ratio; (b) severity coefficient

    图  11  不同权重下的仿真结果图。(a)电机转矩;(b)发动机转矩

    Figure  11.  Simulation results under different weights: (a) motor torque; (b) engine torque

    图  12  不同方法下的SOC曲线

    Figure  12.  SOC curves under different methods

    图  14  不同方法下的仿真结果对比图。(a)发动机转矩;(b)电机转矩对比

    Figure  14.  Comparison diagram of simulation results under different methods: (a) engine torque; (b) motor torque comparison

    图  13  不同方法下的仿真结果对比图。(a)充放电倍率;(b)严重程度系数

    Figure  13.  Comparison figure of simulation results under different methods: (a) charge and discharge ratio; (b) severity coefficient

    表  1  7个US06工况下的仿真结果

    Table  1.   Simulation results of 7 US06 operating conditions

    $ \alpha $Fuel consumption/LEffective ampere-hour flux/(A·h)
    13.941257.6
    0.93.983137.9
    0.84.04676.3
    0.74.10246.9
    0.64.15830.1
    0.54.20021
    0.44.25613.3
    0.34.3267.7
    0.24.4102.8
    0.14.4521.4
    下载: 导出CSV

    表  2  不同方法下的仿真结果对比

    Table  2.   Comparison of simulation results under different methods

    Control strategyFuel consumption/LEffective ampere-hour flux/(A·h)The final SOC
    DP3.983137.90.3032
    ECMS4.016143.20.3010
    下载: 导出CSV
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  • 收稿日期:  2021-03-01
  • 网络出版日期:  2021-07-29

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