基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识  

Estimation of Model Parameters of Lithium Batteries Based on Kalman Filtering Optimized by Dung Beetle Algorithm

作　　者：夏天 刘代飞[2] 岳家辉 陈来恩 李亦梁 XIA Tian;LIU Daifei;YUE Jiahui;CHEN Laien;Li Yiliang(School of Electrical and Information Engineering,ChangSha University of Science and Technology,Changsha 410114,China;School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,China;ChangGao Dianxin Science and Technology Co.,Ltd.,Changsha 410219,China)

机构地区：[1]长沙理工大学电气与信息工程学院,湖南长沙410114 [2]长沙理工大学能源与动力工程学院,湖南长沙410114 [3]长高电新科技股份公司,湖南长沙410219

出　　处：《中国电力》2025年第1期196-204,共9页Electric Power

基　　金：国家自然科学基金资助项目(柔性直流输电交流侧故障下换流器多桥臂主动应对的能量调控机理及穿越控制研究,51977014)。

摘　　要：锂离子电池参数辨识结果是电池状态预测的重要基础,提出了一种基于蜣螂算法(dung beetle optimizer,DBO)优化卡尔曼滤波(Kalman filtering,KF)的方法,用以在线辨识电池模型参数。该方法利用DBO快速全局寻找最优解特点,在KF算法中优化过程噪声和观测噪声的协方差矩阵,提高了识别电池模型参数的准确性。仿真实验数据表明,相较于未优化的KF参数辨识的结果,所提方法辨识误差有明显减少,预测的参数值更加接近真实值。The identification of parameters for lithium batteries is an important basis for battery state prediction.An improved kalman filtering(KF)based on dung beetle optimizer(DBO)is proposed for online identification of battery model parameters.This method utilizes the rapid global search for optimal solutions characteristic of DBO to optimize the covariance matrices of process noise and observation noise in KF,thereby improving the accuracy of identifying battery model parameters.Simulation experiment data shows that compared to the parameter identification results based on unoptimized KF,the variance that the identification results of this method compared to the true values are significantly reduced,resulting in the predicted parameter values are closer to the true values.

关 键 词：锂离子电池 参数辨识 卡尔曼滤波器 蜣螂算法 协方差矩阵 

分 类 号：TM912[电气工程—电力电子与电力传动]