桂林电子科技大学学报2026，Vol.46Issue(2)：177-185,9.DOI:10.16725/j.1673-808X.2023218
基于EKF-LOCR-UKPF算法的电池SOC估计
Battery SOC estimation based on EKF-LOCR-UKPF algorithm
摘要
Abstract
To solve the problems of particle degradation and the limited accuracy of battery state of charge(SOC)estimation of the particle filter algorithm(PF),a joint estimation algorithm combining the online parameter identification of extended Kalman(EKF)at macro time scale and the improved particle filter algorithm(LOCR-UKPF)state estimation at micro time scale based on second-order RC equivalent circuit was studied.UKPF and PF models,and the simulation verification of the algorithms was carried out un-der the conditions of the Federal City Timetable(FUDS)and Highway Timetable(US06).The simulation results show that the Root Mean Square Error(RMSE)of the EKF-LOCR-UKPF algorithm considering the time scale,importance density function and resam-pling strategy is reduced by 21.6%,30.7%and 47.0%compared with the LOCR-UKPF,UKPF and PF algorithms,respectively,and the Root Mean Square Error(RMSE)is reduced by 36.9%,43.8%and 55.4%under the US06 condition,respectively.The improved EKF-LOCR-UKPF joint estimation algorithm has improved the estimation accuracy of battery SOC,and has certain application val-ue and prospects in power battery SOC prediction and battery management.关键词
电池SOC估计/粒子滤波算法/在线辨识/多时间尺度/联合估计Key words
battery SOC estimates/particle filter algorithms/online identification/multiple time scales/joint estimation分类
信息技术与安全科学引用本文复制引用
韩瑞华,范兴明,张鑫..基于EKF-LOCR-UKPF算法的电池SOC估计[J].桂林电子科技大学学报,2026,46(2):177-185,9.基金项目
国家自然科学基金(61741126) （61741126）
广西自然科学基金(2022GXNSFAA035533) （2022GXNSFAA035533）
