机械科学与技术2025，Vol.44Issue(5)：868-877,10.DOI:10.13433/j.cnki.1003-8728.20240045
MSOA算法改进EKF的锂电池SOC估计方法
SOC Estimation of Lithium Battery Using MSOA-optimized EKF Algorithm
摘要
Abstract
The purpose of this paper is to improve the monitoring accuracy of state of charge(SOC)for batteries.Based on the equivalent circuit model of lithiumion batteries,the Elephant herding optimization(EHO)is employed to enhance the identification of model parameters through Kalman filtering(KF).The seagull optimization algorithm(SOA)is utilized to reduce the impact of initial noise values on the extended Kalman filter(EKF)algorithm,while employing an out-of-range processing strategy to avoid the reduction of population diversity.The modified seagull optimization algorithm(MSOA)is applied to optimize the EKF and improve the SOC estimation method for vehicle batteries,which is validated using DST and FDUS dynamic operating current data.The results demonstrate that the improved SOC estimation algorithm yields an error rate lower than 0.97％.Furthermore,the estimated error rates of root mean squared error(RMSE)and mean absolute error(MAE)are both lower than those of the EKF algorithms,indicating that the MSOA-optimized EKF algorithm offers superior estimation accuracy and stability.关键词
锂电池/参数辨识/智能优化算法/荷电状态/扩展卡尔曼滤波Key words
lithium battery/parameter identification/intelligent optimization algorithm/SOC/extended Kalman filter分类
动力与电气工程引用本文复制引用
刘晟,王建锋,刘水宙,潘清云..MSOA算法改进EKF的锂电池SOC估计方法[J].机械科学与技术,2025,44(5):868-877,10.基金项目
中央高校基金项目(300102223203)、陕西省重点研发计划(2021LLRH-04-02-02,2022ZDLGY-03-09)及陕西省厅市联动重点项目(2022GD-TSLD-22) （300102223203）
