文章摘要
刘淑杰,郝昆昆,王永,邓威威.基于改进粒子滤波算法的动力锂离子电池荷电状态估计[J].,2020,60(4):392-401
基于改进粒子滤波算法的动力锂离子电池荷电状态估计
State of charge estimation of power lithium-ion battery based on improved particle filter algorithms
  
DOI：10.7511/dllgxb202004008
中文关键词: 锂离子电池  荷电状态  等效电路模型  参数辨识  粒子滤波  卡尔曼滤波
英文关键词: lithium-ion battery  state of charge  equivalent circuit model  parameters identification  particle filter  Kalman filter
基金项目:国家自然科学基金资助项目(51975100).
作者单位
刘淑杰,郝昆昆,王永,邓威威  
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中文摘要:
      传统电池荷电状态(SOC)估计中常用的扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)方法仅适用于线性系统和高斯条件，虽然粒子滤波(PF)算法能用于非线性和非高斯系统，但PF算法在滤波更新时存在粒子退化现象，使粒子集无法表示实际后验概率分布，导致估计精度降低．采用改进的扩展粒子滤波(EPF)和无迹粒子滤波(UPF)算法对电池SOC进行估计，抑制了粒子权重退化．以Thevenin模型对电池进行建模，利用带遗忘因子的最小二乘方法进行模型参数辨识，结合改进后的滤波算法对电池SOC进行估计．实验结果表明，以UKF为建议密度函数进行重采样的UPF方法平均估计误差为0.71%，低于以EKF为建议密度函数的EPF方法平均误差(1.09%)，两种方法的估计误差均小于PF估计误差(1.36%)，有效抑制了粒子权重退化．
英文摘要:
      In the process of traditional battery state of charge (SOC) estimation, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) methods commonly used are only suitable for linear system and Gaussian environment, although particle filter (PF) algorithm can be applied to non-linear and non-Gaussian systems, there is particle degradation phenomenon in PF algorithm when updating the filter, which makes the particle set unable to truly represent the actual posterior probability distribution, and reduce the estimation accuracy. The improved particle filter algorithms, extended particle filter (EPF) and unscented particle filter (UPF), are used to estimate battery SOC, which can improve the estimation accuracy reduced by particle weight degradation. These algorithms are based on Thevenin battery model, and the least square method with forgetting factor is used to identify the model parameters, combined with the improved particle filter algorithms, the battery SOC is estimated. The experimental results show that the mean error of SOC estimation with UPF using UKF as recommended density function to resample is 0.71%, which is lower than the value of 1.06% obtained with EPF using EKF as recommended density function, these two mean estimation errors are lower than the value of 1.36% under PF estimation, and the effect of restraining particle weight degradation is the most obvious.
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