东南大学学报（英文版）2020，Vol.36Issue(2)：128-137,10.DOI:10.3969/j.issn.1003-7985.2020.02.002
基于修正协方差扩展卡尔曼滤波法的电动汽车锂电池SOC在线估计
Online SOC estimation based on modified covariance extended Kalman filter for lithium batteries of electric vehicles
摘要
Abstract
To offset the defect of the traditional state of charge (SOC) estimation algorithm of lithium battery for electric vehicle and considering the complex working conditions of lithium batteries,an online SOC estimation algorithm is proposed by combining the online parameter identification method and the modified covariance extended Kalman filter (MVEKF) algorithm.Based on the parameters identified on line with the multiple forgetting factors recursive least squares methods,the newly-established algorithm recalculates the covariance in the iterative process with the modified estimation and updates the process gain which is used for the next state estimation to decrease errors of the filter.Experiments including constant pulse discharging and the dynamic stress test (DST) demonstrate that compared with the EKF algorithm,the MVEKF algorithm produces fewer estimation errors and can reduce the errors to 5％ at most under the complex charging and discharging conditions of batteries.In the charging process under the DST condition,the EKF produces a larger deviation and lacks stability,while the MVEKF algorithm can estimate SOC stably and has a strong robustness.Therefore,the established MVEKF algorithm is suitable for complex and changeable working conditions of batteries for electric vehicles.关键词
电动汽车/电池管理系统/锂电池/参数辨识/荷电状态Key words
electric vehicle/battery management system (BMS)/lithium battery/parameter identification/state of charge (SOC)分类
交通工程引用本文复制引用
范家钰,夏菁,陈南,严永俊..基于修正协方差扩展卡尔曼滤波法的电动汽车锂电池SOC在线估计[J].东南大学学报（英文版）,2020,36(2):128-137,10.基金项目
The National Natural Science Foundation of China (No.51375086). （No.51375086）
