现代电子技术2025，Vol.48Issue(10)：7-14,8.DOI:10.16652/j.issn.1004-373x.2025.10.002
基于AFFRLS-MIAUKF算法的锂离子电池SOC估算
Lithium-ion battery SOC estimation based on AFFRLS-MIAUKF algorithm
摘要
Abstract
In the process of estimating the state of charge(SOC)of lithium-ion batteries,establishing a suitable model is the first step,and the accuracy of parameter identification in the model is crucial for estimating SOC.In order to improve the accuracy of lithium-ion battery SOC estimation,an algorithm based on adaptive forgetting factor recursive least squares(AFFRLS)and multi-new adaptive unscented Kalman filter(MIAUKF)is proposed to estimate battery SOC.Taking the ternary lithium-ion battery as the experimental object,the second-order RC equivalent circuit model is established,and the model parameters are identified by means of two identification methods:offline identification and adaptive forgetting factor recursive least squares.Under the condition of hybrid pulse power characteristic(HPPC),AFFRLS-MIAUKF algorithm is used to estimate the SOC of lithium-ion battery,and compare with offline identification MIAUKF algorithm and UKF algorithm.The experimental results show that AFFRLS-MIAUKF algorithm has higher accuracy and the average error can be kept within 0.5%.关键词
锂离子电池/电池荷电状态估算/无迹卡尔曼滤波/自适应遗忘因子递推最小二乘/多新息理论/等效电路模型Key words
lithium-ion battery/battery state of charge estimation/unscented Kalman filter/adaptive forgetting factor recursive least square/multi-new theory/equivalent circuit model分类
电子信息工程引用本文复制引用
王君瑞,李进,季长江,谭露..基于AFFRLS-MIAUKF算法的锂离子电池SOC估算[J].现代电子技术,2025,48(10):7-14,8.基金项目
宁夏自然科学基金项目(2024AAC03182) （2024AAC03182）
国家自然科学基金资助项目(52167004) （52167004）
宁夏回族自治区智能装备与精密检测技术研究应用创新团队(2022BSB03104) （2022BSB03104）
