电池2026，Vol.56Issue(1)：37-45,9.DOI:10.19535/j.1001-1579.2026.01.006
基于模糊FFRLS-IMIUKF的锂离子电池SOC估计
Li-ion battery SOC estimation based on fuzzy FFRLS-IMIUKF
摘要
Abstract
The time-varying characteristics of Li-ion battery parameters and the state of charge(SOC)estimation are susceptible to initial errors and noise interference,a collaborative estimation method combining fuzzy adaptive forgetting-factor recursive least squares(FFRLS)and improved multi-innovation unscented Kalman filter(IMIUKF)is presented.Firstly,based on first-order RC equivalent circuit model,a fuzzy controller is designed to dynamically adjust the forgetting factor of FFRLS,estimate model parameters in real time.Secondly,on the basis of the traditional multi-innovation unscented Kalman filter(MIUKF),the IMIUKF algorithm limits the innovation vector to the current and two previous steps and incorporates a posterior innovation correction mechanism to enhance tolerance to initial SOC errors and process noise.Validation under the urban dynamometer driving schedule(UDDS)driving cycle using NCR-18650GA Li-ion batteries demonstrate that,even with±20％initial SOC error,the proposed method achieves a mean absolute error(MAE)of 3.22％and a root mean square error(RMSE)of 3.16％,outperforming extended Kalman filter(EKF),unscented Kalman filter(UKF)and standard MIUKF algorithms,while maintaining real-time performance.关键词
锂离子电池/荷电状态(SOC)估计/多新息无迹卡尔曼滤波(MIUKF)/遗忘因子递归最小二乘法(FFRLS)/模糊控制Key words
Li-ion battery/state of charge(SOC)estimation/multi-innovation unscented Kalman filter(MIUKF)/forget-ting-factor recursive least square(FFRLS)/fuzzy control分类
信息技术与安全科学引用本文复制引用
陈飞,古素军,曹原,王春生,李日鹏,唐康..基于模糊FFRLS-IMIUKF的锂离子电池SOC估计[J].电池,2026,56(1):37-45,9.基金项目
国家自然科学基金(62103443),中车南京浦镇车辆有限公司科技项目(ZXFW001202400091) （62103443）
