State of Charge Estimation for Lithium-Ion Battery Based on Improved Cubature Kalman Filter Algorithm  

作　　者：Guochun Li Chang Liu Enlong Wang Limei Wang 

机构地区：[1]Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013,China [2]Institute for Energy Research,Jiangsu University,Zhenjiang 212013,China

出　　处：《Automotive Innovation》2021年第2期189-200,共12页汽车创新工程（英文）

基　　金：the National Natural Science Foundation of China(52072155,51707084);the Six Talent Peaks Project in Jiangsu Province(2018-XNYQC-004);the Open Research Subject of Key Laboratory of Automotive Measurement and Control and Safety(QCCK2020-009);the Natural Science Research Project of Jiangsu Higher Education Institutions(19KJB470013);the Young Talent Cultivation Project of Jiangsu University.

摘　　要：An improved cubature Kalman filter(CKF)algorithm for estimating the state of charge of lithium-ion batteries is proposed.This improved algorithm implements the diagonalization decomposition of the covariance matrix and a strong tracking filter.First,a first-order RC equivalent circuit model is first established and verified,whose voltage estimation error is within 1.5%;this confirms that the model can be used to describe the characteristics of a battery.Then the calculation processes of the traditional and proposed CKF algorithms are compared.Subsequently,the improved CKF algorithm is applied to the state of charge estimation under the constant-current discharge and dynamic stress test conditions.The average errors for these two conditions are 0.76%and 1.2%,respectively,and the maximum absolute error is only 3.25%.The results indicate that the proposed method has higher filter stability and estimation accuracy than the extended Kalman filter(EKF),unscented Kalman filter(UKF)and traditional CKF algorithms.Finally,the convergence rates of the above four algorithms are compared,among which the proposed algorithm track the referenced values at the highest speed.

关 键 词：Lithium-ion battery State of charge Cubature Kalman filter Strong tracking filter Covariance matrix diagonalization decomposition 

分 类 号：TM912[电气工程—电力电子与电力传动]