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基于融合模型的锂离子电池荷电状态在线估计

王晓兰 靳皓晴 刘祥远

王晓兰, 靳皓晴, 刘祥远. 基于融合模型的锂离子电池荷电状态在线估计[J]. 工程科学学报, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001
引用本文: 王晓兰, 靳皓晴, 刘祥远. 基于融合模型的锂离子电池荷电状态在线估计[J]. 工程科学学报, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001
WANG Xiao-lan, JIN Hao-qing, LIU Xiang-yuan. Online estimation of the state of charge of a lithium-ion battery based on the fusion model[J]. Chinese Journal of Engineering, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001
Citation: WANG Xiao-lan, JIN Hao-qing, LIU Xiang-yuan. Online estimation of the state of charge of a lithium-ion battery based on the fusion model[J]. Chinese Journal of Engineering, 2020, 42(9): 1200-1208. doi: 10.13374/j.issn2095-9389.2019.09.20.001

基于融合模型的锂离子电池荷电状态在线估计

doi: 10.13374/j.issn2095-9389.2019.09.20.001
详细信息
    通讯作者:

    E-mail:wangzt@lut.cn

  • 中图分类号: TM911.3

Online estimation of the state of charge of a lithium-ion battery based on the fusion model

More Information
  • 摘要: 针对锂离子电池荷电状态(Stage of charge,SOC)在线估计精度不高,等效电路模型法估计精度与模型复杂度相矛盾的问题,本文对扩展卡尔曼滤波算法进行了改进,并以电池工作电压、电流为输入,对应等效电路模型法的SOC估计误差为输出,采用极限学习机算法,建立基于输入输出数据的SOC估计误差预测模型,采用物理–数据融合方法,基于误差预测模型,建立了等效电路模型法结合极限学习机的锂离子电池SOC在线估计模型。仿真结果表明,改进扩展卡尔曼滤波算法提高了算法的估计精度,而物理–数据融合的锂离子电池SOC在线估计模型减小了由电压、电流测量所引入的估计误差,克服了等效电路模型法估计精度与模型复杂度之间相矛盾的问题,进一步提高了SOC的估计精度,满足估计误差不超过5%的应用需求。
  • 图  1  电池模块原理图

    Figure  1.  Schematic of the battery module

    图  2  一阶Thevenin等效电路模型

    Figure  2.  First-order Thevenin equivalent circuit model

    图  3  改进EKF算法估计SOC流程图

    Figure  3.  Flowchart of the improved extended Kalman filtering (EKF) algorithm used to estimate the state of charge (SOC)

    图  4  EKF算法误差对比曲线

    Figure  4.  Error contrast curve of the EKF algorithm

    图  5  基于ELM的SOC误差预测模型结构

    Figure  5.  Structure of the SOC error prediction model based on the extreme learning machine algorithm

    图  6  预测模型在测试集下的绝对误差值

    Figure  6.  Absolute error of the prediction model under the test set

    图  7  融合模型法系统结构图

    Figure  7.  System structure diagram of the fusion model method

    图  8  绝对误差对比

    Figure  8.  Comparison of the absolute errors

    图  9  二阶Thevenin等效电路模型

    Figure  9.  Second-order Thevenin equivalent circuit model

    图  10  SOC估计曲线对比

    Figure  10.  Comparison of the SOC estimation curves

    表  1  第一组放电试验数据

    Table  1.   First set of discharge data

    Current, I/AVoltage, UL/VStandard value of SOCS
    4.9914.1101
    6.8274.0891
    6.5014.0840.999
    $ \vdots $$ \vdots $$ \vdots $
    57.7703.4200.665
    45.9623.3910.664
    $ \vdots $$ \vdots $$ \vdots $
    29.6993.1090.156
    25.5373.0720.155
    下载: 导出CSV

    表  2  第二组放电试验数据

    Table  2.   Second set of discharge data

    Current,$I$/AVoltage,${U_{\rm{L}}}$/VStandard value of SOCs
    4.9924.1091
    6.8264.0871
    6.5014.0840.999
    $ \vdots $$ \vdots $$ \vdots $
    52.7303.4210.664
    42.1853.4300.662
    $ \vdots $$ \vdots $$ \vdots $
    33.1223.0830.156
    32.5832.9790.155
    下载: 导出CSV

    表  3  一阶Thevenin等效电路模型参数

    Table  3.   Parameters of the first-order Thevenin equivalent circuit model

    ${R_0}$/Ω${R_1}$/Ω${C_1}$/F
    0.00560.00725631.8
    下载: 导出CSV

    表  4  传统EKF算法与改进EKF算法均方误差对比

    Table  4.   Comparison of the mean squared error between the traditional and improved extended Kalman filtering (EKF) algorithms

    AlgorithmMean squared error
    Traditional EKF algorithm2.188 × 10–3
    Improved EKF algorithm9.899 × 10–4
    下载: 导出CSV

    表  5  SOC估计绝对误差

    Table  5.   Absolute error of the state of charge estimation

    First groupSecond group
    00
    1.052 × 10–41.053 × 10–4
    9.685 × 10–59.680 × 10–5
    $ \vdots $$ \vdots $
    0.0270.032
    0.0270.032
    $ \vdots $$ \vdots $
    0.0420.047
    0.0430.047
    下载: 导出CSV

    表  6  基于ELM的误差预测模型性能

    Table  6.   Error prediction of model line performance based on the extreme learning machine algorithm

    Decisive factorMean square errorTraining time/s
    0.483 × 10–55.05
    下载: 导出CSV

    表  7  二阶Thevenin等效电路参数

    Table  7.   Parameters of the second-order Thevenin equivalent circuit model

    R0R1R2C1/FC2/F
    0.00550.00410.0017217973634
    下载: 导出CSV

    表  8  不同模型估计结果对比

    Table  8.   Comparison of the estimation results of different models

    Model Mean square error Maximum absolute error Maximum percent error/%
    First-order Thevenin model 9.89 × 10–4 0.05 0.3
    Second-order Thevenin model 4.98 × 10–4 0.03 0.10
    Fusion model 3.01 × 10–5 0.02 0.09
    下载: 导出CSV
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  • 收稿日期:  2019-09-20
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