电器与能效管理技术Issue(6)：49-58,10.DOI:10.16628/j.cnki.2095-8188.2024.06.008
基于改进EKF算法的锂离子电池SOC在线估计
Online State of Charge Estimation of Lithium-ion Battery Based on Improved Extended Kalman Filter Algorithm
摘要
Abstract
Lithium-ion batteries have been widely used in the field of energy storage power stations due to their high energy density,low self-discharge rate and low pollution.To solve the accurate prediction of various states of lithium-ion batteries,a second-order RC equivalent circuit model is first built,and then the parameters of the model are identified by using the forgetting factor recursive least squares(FFRLS)method.A joint SOC-SOH estimation method based on adaptive extended Kalman filtering(AEKF)algorithm is proposed,and the method is compared and verified under different battery conditions.Experimental results show that compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF),the proposed method can improve the accuracy and computational efficiency of SOC and SOH prediction,and has certain practical value.关键词
锂离子电池/二阶RC模型/参数辨识/SOH估计/SOC估计/AEKF算法Key words
lithium battery/second-order RC model/parameter identification/SOH estimation/SOC estimation/AEKF algorithm分类
信息技术与安全科学引用本文复制引用
崔晓丹,吴家龙,邓馗,王彦品,冯佳期,李亚杰..基于改进EKF算法的锂离子电池SOC在线估计[J].电器与能效管理技术,2024,(6):49-58,10.基金项目
国网南瑞科技股份有限公司科技项目《锂电池储能系统电磁暂态模型构建技术研究》 （）
