电源学报2026，Vol.24Issue(5)：159-167,9.DOI:10.13234/j.issn.2095-2805.2026.5.159
基于粒子和容积卡尔曼滤波的锂离子电池SOC估计
SOC Estimation of Lithium-ion Batteries Based on Particle and Cubature Kalman Filters
摘要
Abstract
Accurate estimation of State of Charge(SOC)for lithium-ion batteries is crucial for implementing battery balancing during charging and discharging,thus extending battery lifespan.Given the complex chemical characteristics of lithium-ion batteries and the non-time-varying nature of SOC estimation using the Kalman filter algorithm,a novel method for SOC estimation for lithium-ion batteries is proposed.Firstly,combining the precision and practicality of lithium-ion battery models,a second-order equivalent model for lithium-ion batteries is presented.Subsequently,utilizing the Adaptive Forgetting Factor Recursive Least Squares(AFFRLS)algorithm with adaptive forgetting factors,the impedance-capacitance parameters of the second-order model are identified online,establishing a filtering relationship for impedance-capacitance parameters.The proposed method integrates Particle Filter(PF)and Cubature Kalman Filter(CKF)algorithms,termed Particle Cubature Kalman Filter(PF-CKF),to estimate SOC for lithium-ion batteries.Finally,Hybrid Pulse Power Characterization(HPPC)experiments are designed to validate the accuracy and stability of this method.关键词
锂离子电池/荷电状态估计/自适应遗忘因子/改进粒子滤波/容积卡尔曼滤波Key words
Lithium-ion battery/state of charge/adaptive forgetting factor/improved particle filter/cubature Kalman filter分类
信息技术与安全科学引用本文复制引用
彭云海,张尧,张帆,于龙杰..基于粒子和容积卡尔曼滤波的锂离子电池SOC估计[J].电源学报,2026,24(5):159-167,9.基金项目
国家自然科学基金面上项目(51877058) （51877058）
浙江省"尖兵"研发攻关计划资助项目(2024C01018)This work is supported by General Progran of National Natural Science Foundation of China under the grant 51877058 （2024C01018）
Zhejiang Province Pioneer Plan Project under the grant 2024C01018 （）
