电源技术2026，Vol.50Issue(4)：654-661,8.DOI:10.3969/j.issn.1002-087X.2026.04.09
基于双调整因子与自适应滑动窗口的EKF电池SOC估计方法
Battery SOC estimation method based on dual adjustment factors and adaptive sliding window for EKF
摘要
Abstract
State-of-charge(SOC)estimation is a critical component of battery management systems(BMS).To enhance the accuracy and robustness of battery SOC estimation,an improved Kalman fil-ter method was proposed that employed dual adjustment factors for adaptive adjustment to process noise and observation noise.Simultaneously,the sliding window size was updated adaptively based on covariance matching and current change intensity.Simulations were conducted on the VSCODE/Python platform under three typical operating conditions:DST,FUDS,and US06.Across all three scenarios,the proposed method achieves an MAE below 1.44%and an RMSE below 1.55%,demon-strating its effectiveness in enhancing the precision and stability of battery SOC estimation.关键词
电荷状态/电池管理系统/协方差匹配技术/自适应调整/鲁棒性Key words
state-of-charge/battery management system/covariance matching technique/adaptive adjustment/robustness分类
信息技术与安全科学引用本文复制引用
张宇,黄鹏,吴铁洲..基于双调整因子与自适应滑动窗口的EKF电池SOC估计方法[J].电源技术,2026,50(4):654-661,8.基金项目
国家自然科学基金项目(52377207) （52377207）
