SOC Estimation Method for Lithium-ion Batteries: Extended Kalman Filter with Weighted Innovation

被引：0
|
作者：
Han, Yiyang [1 ]
Ding, Jie [1 ]
Chen, Jiazhong [1 ]
Sun, Peng [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Sch Automat, Nanjing 210023, Peoples R China
来源：
PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年
关键词：
Lithium-ion Batteries; State of Charge; Second-order Thevenin Model; Weighted Innovation Extended Kalman Filter; CHARGE ESTIMATION; STATE; MANAGEMENT;
D O I：
10.1109/ccdc.2019.8833412
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper considers the second-order Thevenin model to describe dynamics of Lithium-ion batteries and identifies the system parameters by current pulse experiments. In general, extended Kalman filter (EKF) algorithms are used to estimate the state of charge (SOC), but they utilize the current innovation and ignore the previous data. In order to improve the estimation accuracy of the SOC and make full use of information, a weighted innovation extended Kalman filter (WI-EKE) algorithm is proposed. It combines the weight calculation idea from particle filter with multiple innovations and allocates the weight of each innovation according to its importance. Experimental results show that the method is able to effectively improve the accuracy of SOC estimation, and its maximum error is less than 1%, which can meet the requirements of battery management system.
引用
收藏
页码：5074 / 5078
页数：5
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