Improved sliding mode based EKF for the SOC estimation of lithium-ion batteries

被引：0
|
作者：
Liang Feng
Jie Ding
Yiyang Han
机构：
[1] Nanjing University of Posts and Telecommunications,Jiangsu Engineering Lab for IOT Intelligent Robots, School of Automation and Artificial Intelligence
来源：
Ionics | 2020年 / 26卷
关键词：
Discrete sliding mode observer; Weighted innovation extended Kalman filter; State of charge estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper combines the discrete sliding mode observer with the weighted innovation extended Kalman filter to improve the accuracy of the SOC estimation. The main work of this paper can be divided into two parts: (1) The proposed algorithms utilize the previous information and the current innovation by choosing proper weights to estimate the SOC accurately. (2) The improved discrete sliding mode observer is introduced into the weighted innovation extended Kalman filter to solve the chattering problem. The experimental results show that the accuracy of the SOC estimation is improved effectively.
引用
收藏
页码：2875 / 2882
页数：7
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