摘要
该文旨在提高电池荷电状态(State of charge, SOC)的监测精度,基于锂离子等效电路模型,采用象群优化算法改进卡尔曼滤波(Kalman filter, KF)进行模型参数的辨识;使用海鸥算法(Seagull optimization algorithm, SOA)降低噪声初值对扩展卡尔曼滤波(Extended Kalman filter, EKF)算法的影响,并以越界处理的策略避免种群多样性降低的问题;以改进海鸥算法(Modified seagull optimization algorithm, MSOA)优化EKF来改善车载电池SOC估计方法,将DST和FUDS动态测试工况电流数据进行算法验证。结果表明,改进后的SOC估计算法的误差低于0.97%,且平均绝对误差(Mean absolute error, MAE)和均方根误差(Root mean squared error, RMSE)均低于EKF算法的估计误差,因此MSOA优化EKF算法具有更好的估计精度和稳定性。
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.
作者
刘晟
王建锋
刘水宙
潘清云
LIU Sheng;WANG Jianfeng;LIU Shuizhou;PAN Qingyun(School of Automobile,Chang'an University,Xi'an 710064,China)
出处
《机械科学与技术》
北大核心
2025年第5期868-877,共10页
Mechanical Science and Technology for Aerospace Engineering
基金
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)
陕西省厅市联动重点项目(2022GD-TSLD-22)。
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
lithium battery
parameter identification
intelligent optimization algorithm
SOC
extended Kalman filter
