江苏大学学报（自然科学版）2024，Vol.45Issue(1)：24-29,6.DOI:10.3969/j.issn.1671-7775.2024.01.004
基于卡尔曼滤波的动力电池SOC估算
SOC estimation of power battery based on Kalman filter
摘要
Abstract
Due to the limitations of traditional unscented Kalman filter estimation methods,to accurately estimate the state of charge(SOC)of power battery,a method for SOC estimation of power battery based on unscented Kalman particle filter was proposed.Taking the ternary lithium battery as research object,the second-order RC equivalent circuit model of the battery was established to identify the model parameters through the battery charging and discharging test,and the accuracy of the model was verified.The battery data under actual working conditions were collected,and the SOC was estimated by untracked Kalman filter algorithm,particle filter algorithm and untracked Kalman particle filter algorithm,respectively.The simulation experiments were carried out in MATLAB,and the estimated SOC values were compared.The results show that the untracked Kalman particle filter algorithm can estimate the SOC quickly and accurately,and the error is less than 2.5%,which is better than those of other two algorithms.关键词
三元锂电池/SOC/等效电路模型/卡尔曼滤波/电池管理系统Key words
ternary lithium battery/SOC/equivalent circuit model/Kalman filter/battery management system分类
交通工程引用本文复制引用
徐立友,马可,杨晴霞,宋林涛,马小斌..基于卡尔曼滤波的动力电池SOC估算[J].江苏大学学报（自然科学版）,2024,45(1):24-29,6.基金项目
国家重点研发计划项目(2016YFD0701002) （2016YFD0701002）
河南省科技攻关项目(212102210328) （212102210328）
