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VCM模型下的IBAS-EKF锂电池荷电状态估计

寇发荣 王思俊 王甜甜 洪锋 杨慧杰

寇发荣, 王思俊, 王甜甜, 洪锋, 杨慧杰. VCM模型下的IBAS-EKF锂电池荷电状态估计[J]. 机械科学与技术, 2021, 40(12): 1929-1938. doi: 10.13433/j.cnki.1003-8728.20200287
引用本文: 寇发荣, 王思俊, 王甜甜, 洪锋, 杨慧杰. VCM模型下的IBAS-EKF锂电池荷电状态估计[J]. 机械科学与技术, 2021, 40(12): 1929-1938. doi: 10.13433/j.cnki.1003-8728.20200287
KOU Farong, WANG Sijun, WANG Tiantian, HONG Feng, YANG Huijie. IBAS-EKF Estimation of Lithium Battery State of Charge under VCM[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1929-1938. doi: 10.13433/j.cnki.1003-8728.20200287
Citation: KOU Farong, WANG Sijun, WANG Tiantian, HONG Feng, YANG Huijie. IBAS-EKF Estimation of Lithium Battery State of Charge under VCM[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(12): 1929-1938. doi: 10.13433/j.cnki.1003-8728.20200287

VCM模型下的IBAS-EKF锂电池荷电状态估计

doi: 10.13433/j.cnki.1003-8728.20200287
基金项目: 

国家自然科学基金项目 51775426

陕西省重点研发计划项目 2020GY-128

西安市科技计划项目 21XJZZ0039

详细信息
    作者简介:

    寇发荣(1973-), 教授, 博士, 研究方向为车辆动力学及电池管理系统, koufarong@xust.edu.cn

  • 中图分类号: TM912

IBAS-EKF Estimation of Lithium Battery State of Charge under VCM

  • 摘要: 为解决锂电池荷电状态(SoC)难以精确估计的问题,提出了极化电压修正模型(VCM)和改进天牛须优化扩展Kalman滤波算法(IBAS-EKF)共同实现电池SoC的精确估计。在建立3阶RC电池模型和参数辨识的基础上,使用Elman循环神经网络对模型极化电压实现在线修正和优化,形成VCM模型;采用改进天牛须搜索算法优化扩展Kalman滤波算法的系统噪声协方差矩阵和量测噪声协方差矩阵,形成IBAS-EKF锂电池SoC估计算法。在测试平台上进行城市道路循环工况试验,结果表明:基于VCM模型的IBAS-EKF锂电池SoC估计算法的各项误差指标均低于传统的SoC估计算法,估计误差在0.6%以内,效果满足实际工程要求。
  • 图  1  3阶RC等效电路模型

    图  2  基于Elman神经网络的极化电压修正流程

    图  3  IBAS优化EKF噪声协方差算法

    图  4  改进天牛须搜索算法最优适应度迭代曲线

    图  5  ITECH动力电池测试系统

    图  6  常规电性试验步骤

    图  7  联合多模型OCV-SoC曲线

    图  8  混合动力脉冲测试电压、电流

    图  9  分段三次Hermite法插值3阶RC模型曲线

    图  10  美国城市道路循环工况

    图  11  极化电压修正模型、3阶RC模型端电压与试验电压对比曲线及其误差

    图  12  UDDS工况下各模型负载端电压误差统计分析

    图  13  IBAS-EKF算法、传统EKF算法估计SoC值与SoC试验值对比曲线及误差

    图  14  UDDS工况下锂电池SoC估计误差统计分析

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  • 收稿日期:  2020-06-03
  • 刊出日期:  2021-12-05

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