电源学报2026，Vol.24Issue(5)：149-158,10.DOI:10.13234/j.issn.2095-2805.2026.5.149
基于分数阶模型和FOMICKF-FOUKF算法的锂电池SOC估计
SOC Estimation of Lithium-ion Battery Based on Fractional Order Model and FOMICKF-FOUKF Algorithm
摘要
Abstract
Aiming at the problems that the offline parameter identification method can only identify a single fixed parameter which leads to insufficiently accurate state of charge(SOC)estimation,and that the traditional Kalman filtering algorithm is poorly adapted to different temperatures,a fractional-order multi-innovation cubature Kalman filtering combined with fractional-order unscented Kalman filtering(FOMICKF-FOUKF)algorithm is proposed to estimate the SOC of lithium-ion batteries.The algorithm adopts a multi-timescale approach to carry out the online parameter identification with the FOUKF in the macro time scale,and the SOC estimation with the FOMICKF in the micro time scale.The convergence ability of different algorithms varies,and in order to verify the convergence of the algorithms,the algorithms were subjected to the convergence comparison experiment.In addition,it is more difficult to accurately estimate battery SOC at different temperatures where the internal polarization reaction varies significantly.SOC estimation experiments were conducted at 0℃,25℃ and 45℃ to verify the adaptability of the proposed algorithm to different temperatures.The results show that the FOMICKF-FOUKF dual Kalman online joint algorithm estimates the SOC with high accuracy and good convergence,and has good adaptability to different temperatures.关键词
荷电状态估计/分数阶/多新息容积卡尔曼滤波/联合估计Key words
State of charge estimation/fractional order/multi-innovation cubature Kalman filter/joint estimation分类
信息技术与安全科学引用本文复制引用
宋杨皖豪,李昕..基于分数阶模型和FOMICKF-FOUKF算法的锂电池SOC估计[J].电源学报,2026,24(5):149-158,10.基金项目
安徽省高校自然科学基金资助项目(KJ2019A0106) （KJ2019A0106）
2020 年安徽省教育厅资助项目(2020JYXM0460)This work is supported by Natural Science Foundation of Anhui Province Universities under the grant KJ2019A0106 （2020JYXM0460）
2020 Anhui Provincial Department of Education Project under the grant 2020JYXM0460 （）
