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基于GWO-LSSVM的锂离子电池健康状态估算

李炬晨 胡欲立 高剑 曾立腾 郑乙 代文帅

李炬晨, 胡欲立, 高剑, 等. 基于GWO-LSSVM的锂离子电池健康状态估算[J]. 水下无人系统学报, 2022, 30(5): 550-557 doi: 10.11993/j.issn.2096-3920.202109007
引用本文: 李炬晨, 胡欲立, 高剑, 等. 基于GWO-LSSVM的锂离子电池健康状态估算[J]. 水下无人系统学报, 2022, 30(5): 550-557 doi: 10.11993/j.issn.2096-3920.202109007
LI Ju-chen, HU Yu-li, GAO Jian, ZENG Li-teng, ZHENG Yi, DAI Wen-shuai. State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 550-557, 566. doi: 10.11993/j.issn.2096-3920.202109007
Citation: LI Ju-chen, HU Yu-li, GAO Jian, ZENG Li-teng, ZHENG Yi, DAI Wen-shuai. State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM[J]. Journal of Unmanned Undersea Systems, 2022, 30(5): 550-557, 566. doi: 10.11993/j.issn.2096-3920.202109007

基于GWO-LSSVM的锂离子电池健康状态估算

doi: 10.11993/j.issn.2096-3920.202109007
详细信息
    作者简介:

    李炬晨(1996-), 男, 在读博士, 主要研究方向为能源与动力及能量管理策略

  • 中图分类号: U674; TM912

State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM

  • 摘要: 针对目前应用于电池健康状态(SOH)估算的算法需用大量数据样本来进行训练且估算效果不佳的问题, 提出了一种基于灰狼优化(GWO)算法的最小二乘支持向量机(LSSVM)算法来估算电池SOH, 依据灰色关联度分析法筛选出恒流充电时间作为适合估算电池SOH的输入特征参数。以18650钴酸锂电池充放电循环试验为例, 采用所建立的算法模型在不同比例的训练集样本下对不同容量规格的电池进行SOH估算, 并将估算结果与基于网格搜索法的LSSVM算法、基于粒子群优化算法的LSSVM算法的估算结果进行对比, 结果表明, 基于GWO算法的LSSVM算法模型适用于小样本数据且估算误差较小, 用于电池SOH估算的效果更好。

     

  • 图  1  基于数据驱动的估算模型框架

    Figure  1.  Framework of data-driven estimating model

    图  2  GWO-LSSVM估算模型流程图

    Figure  2.  Flow chart of GWO-LSSVM estimating model

    图  3  充放电循环实验现场

    Figure  3.  Charge/discharge cycle test site

    图  4  电池充放电循环制度流程图

    Figure  4.  Flow chart of battery charge/discharge cycle system

    图  5  电池放电容量衰减曲线

    Figure  5.  Curves of battery discharge capacity attenuation

    图  6  特征参数变化曲线

    Figure  6.  Curves of characteristic parameters

    图  7  第1组电池的估算结果与真实值对比

    Figure  7.  Comparison between estimated results and actual values of batteries from group 1

    图  8  第2组电池的估算结果与真实值对比

    Figure  8.  Comparison between estimated results and actual values of batteries from group 2

    图  9  不同算法在训练样本为30%时的估算结果对比

    Figure  9.  Comparison of estimated results by different algorithms when the training sample proportion is 30%

    图  10  不同算法在训练样本为60%时的估算结果对比

    Figure  10.  Comparison of estimated results by different algorithms when the training sample proportion is 60%

    图  11  不同算法在训练样本为90%时的估算结果对比

    Figure  11.  Comparison of estimated results of different algo- rithms when the training sample proportion is 90%

    表  1  各个参数与容量的关联度均值

    Table  1.   Mean value of relational grade between capacity and each factor

    参数关联度
    恒流充电时间0.939 6
    充电时间0.902 5
    充电30 min时的端电压0.883 6
    等压段充电时间0.819 7
    恒压充电时间0.803 0
    下载: 导出CSV

    表  2  第1组电池的超参数寻优结果

    Table  2.   Optimization results of hyper-parameter of batteries from group 1

    超参数训练集占比
    30%60%90%
    c$ 7.95 \times {10^3} $$ 4.97 \times {10^4} $$ 1.42 \times {10^4} $
    σ$ 5.59 \times {10^7} $$ {10^8} $$ 9.99 \times {10^7} $
    下载: 导出CSV

    表  3  第1组电池的估算精度对比

    Table  3.   Comparison of estimation accuracy for batteries from group 1

    指标训练集占比
    30%60%90%
    RMSE0.01970.01800.0178
    MAE0.01420.01370.0136
    下载: 导出CSV

    表  4  第2组电池的超参数寻优结果

    Table  4.   Optimization results of hyperparameter of batteries from group 2

    超参数估算
    估算1估算2估算3
    c$ 1.02 \times {10^7} $$ 8.23 \times {10^6} $$ 1.04 \times {10^6} $
    σ$ {10^8} $$ {10^8} $$ {10^8} $
    下载: 导出CSV

    表  5  第2组电池的估算精度对比

    Table  5.   Comparison of estimation accuracy for batteries from group 2

    指标估算
    估算1估算2估算3
    RMSE0.02000.01230.004
    MAE0.01770.01050.0029
    下载: 导出CSV

    表  6  2组电池在不同算法不同训练集样本下的估算性能指标对比

    Table  6.   Comparison of estimation performance specifications of different algorithms for the two groups of batteries with different proportions of training set samples

    电池优化
    算法
    训练集样本比例RMSEMAE
    1PSO-LSSVM30%0.04400.0345
    60%0.02630.0212
    90%0.02030.0157
    GWO-LSSVM30%0.01970.0142
    60%0.01800.0137
    90%0.01780.0136
    2PSO-LSSVM30%0.02730.0246
    60%0.01260.0105
    90%0.00400.0029
    GWO-LSSVM30%0.02000.0177
    60%0.01230.0105
    90%0.00400.0029
    下载: 导出CSV
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    Zhang Jin-long, Tong Wei, Sun Ye-ning, et al. Summarize of Lithium Battery Status of Health Estimation Method[J]. Journal of Power Supply, 2017, 15(2): 128-134.
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    [6] 何发尧, 胡欲立, 郭广华, 等. 基于人工神经网络估算锂离子电池的SOH[J]. 电源技术, 2017, 41(5): 708-710. doi: 10.3969/j.issn.1002-087X.2017.05.013

    He Fa-yao, Hu Yu-li, Guo Guang-hua, et al. State of Health Estimation for Lithium-ion Batteries Based on ANN[J]. Chinese Journal of Power Sources, 2017, 41(5): 708-710. doi: 10.3969/j.issn.1002-087X.2017.05.013
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    Liu Si-feng, Cai Hua, Yang Ying-jie, et al. Advance in Grey Incidence Analysis Modelling[J]. Systems Engineerings-Theory & Practice, 2013, 33(8): 2041-2046. doi: 10.3969/j.issn.1000-6788.2013.08.018
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出版历程
  • 收稿日期:  2021-09-10
  • 修回日期:  2022-01-06
  • 网络出版日期:  2022-09-15

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