Modeling and state of charge estimation of lithium-ion battery  

作　　者：Xi-Kun Chen Dong Sun 

机构地区：[1]School of Mechatronics Engineering and Automation,Shanghai University

出　　处：《Advances in Manufacturing》2015年第3期202-211,共10页先进制造进展（英文版）

基　　金：supported by the National High Technology Research and Development of China 863 Program(Grant No. 2011AA11A247)

摘　　要：Modeling and state of charge （SOC） estimation of lithium-ion （Li-ion） battery are the key techniques of battery pack management system （BMS） and critical to its reliability and safety operation. An auto-regressive with exogenous input （ARX） model is derived from RC equivalent circuit model （ECM） due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square （VFFRLS） algorithm is adopted as an adaptive parameter identifica- tion method. Based on the designed model, an SOC estimator using cubature Kalman filter （CKF） algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter （EKF） algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.Modeling and state of charge （SOC） estimation of lithium-ion （Li-ion） battery are the key techniques of battery pack management system （BMS） and critical to its reliability and safety operation. An auto-regressive with exogenous input （ARX） model is derived from RC equivalent circuit model （ECM） due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square （VFFRLS） algorithm is adopted as an adaptive parameter identifica- tion method. Based on the designed model, an SOC estimator using cubature Kalman filter （CKF） algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter （EKF） algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.

关 键 词：Lithium-ion （Li-ion） battery Variable forgetting factor recursive least square （VFFRLS） Cubature Kalman filter （CKF） Extended Kalman filter （EKF） 

分 类 号：TM912[电气工程—电力电子与电力传动] TM744