电源技术2026，Vol.50Issue(4)：662-671,10.DOI:10.3969/j.issn.1002-087X.2026.04.10
基于自适应双重遗忘因子的Sage-Husa算法SOE估算研究
A study on state of energy estimation using the Sage-Husa algorithm based on adaptive dual forgetting factors
摘要
Abstract
State of energy(SOE)estimation is one of the core functions of a battery management sys-tem(BMS).This paper addressed the limitations of the traditional Sage-Husa algorithm in terms of noise covariance estimation instability and insufficient long-term convergence.It proposed an im-proved method based on an adaptive dual forgetting factor and introduced covariance matching tech-nology as an auxiliary constraint to enhance the stability of noise modelling and the long-term conver-gence of estimation.Based on this,combined with a second-order RC equivalent circuit model and forgetting factor recursive least squares(FFRLS)online identification,simulations were conducted on three typical operating conditions(DST,FUDS,and US06)in the Simulink platform.The results show that the estimated curves converge rapidly and avoid long-term drift,with errors maintained within 2.5%,verifying the higher accuracy and robustness of this algorithm compared with the tradi-tional Sage-Husa algorithm.关键词
能量状态/电池管理系统/Sage-Husa算法/自适应双重遗忘因子/协方差匹配技术/鲁棒性Key words
state of energy/battery management system/Sage-Husa algorithm/adaptive dual forget-ting factor/covariance matching technique/robustness分类
信息技术与安全科学引用本文复制引用
王超,洪海强,许逸,吴铁洲,潘奎廷,朱凌寒..基于自适应双重遗忘因子的Sage-Husa算法SOE估算研究[J].电源技术,2026,50(4):662-671,10.基金项目
国家自然科学基金项目(52377207) （52377207）
