东北电力技术2026，Vol.47Issue(8)：28-34,7.
一种改进的AEKF算法估算电池的SOC
An Improved AEKF Algorithm for Estimating SOC of Batteries
徐俊江 1袁学庆 2刘军3
作者信息
- 1. 沈阳理工大学自动化与电气工程学院,辽宁 沈阳 110159||中国科学院沈阳自动化研究所,辽宁 沈阳 110167
- 2. 中国科学院沈阳自动化研究所,辽宁 沈阳 110167
- 3. 沈阳理工大学自动化与电气工程学院,辽宁 沈阳 110159
- 折叠
摘要
Abstract
In order to improve the accuracy of battery state of charge(SOC)calculation,enhancing the accuracy of battery parameter identification and the precision of SOC estimation is the core key.To address the limitation of fixed battery parameters in traditional methods,first of all,it adopts the forget factor recursive least squares(FFRLS)method combined with the improved particle swarm optimization(PSO)algorithm.It identifies and optimizes the second-order equivalent circuit parameters of the battery to obtain the optimal parameter values.Secondly,it optimizes the extended kalman filter(EKF)method and uses the adaptive extended kalman filter(AEKF)to accurately estimate the battery SOC.It collects data of 18650 batteries through the hybrid pulse power characteristic test.Finally,it compares the experimental data of this method with those of the existing FFRLS-EKF methods.The results show that in the parameter identification stage,the adopted optimized algorithm makes the estimation of terminal voltage closer to the actual terminal voltage compared with the proposed method.In the SOC estimation stage,the proposed algorithm can improve the accuracy of SOC estimation.In conclusion,the FFRLS-PSO-AEKF method improves the accuracy of battery parameter identification and SOC estimation,providing reliable technical support for the development of high-performance battery management systems.关键词
参数辨识/电池荷电状态/粒子群优化算法/自适应扩展卡尔曼滤波Key words
parameter identification/SOC/PSO/AEKF分类
信息技术与安全科学引用本文复制引用
徐俊江,袁学庆,刘军..一种改进的AEKF算法估算电池的SOC[J].东北电力技术,2026,47(8):28-34,7.
