Lithium-ion battery SOC estimation based on an improved adaptive extended Kalman filter

被引：5
|
作者：
Wang, Yunqiu [1 ]
Li, Lei [1 ]
Ding, Quansen [1 ]
Liu, Jiale [1 ]
Chen, Pengwei [1 ]
机构：
[1] Nanjing Univ Sci & Technol, Coll Automat, Nanjing, Peoples R China
来源：
PROCEEDINGS OF THE 2021 IEEE 16TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2021) | 2021年
关键词：
Lithium-ion battery; equivalent circuit model; SOC estimation; parameter identification; extended Kalman filter;
D O I：
10.1109/ICIEA51954.2021.9516403
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Battery is an important driving force of electric vehicles. Reasonable utilization of battery energy is a key link of battery management system. The battery management system can ensure the safety and efficiency of the battery by accurately estimating the SOC of the battery. In this paper, based on the establishment of battery equivalent model and parameter identification, a new battery SOC estimation method is proposed. This method is improved on the extended Kalman filter, and an adaptive filtering algorithm is used to solve the noise problem. Firstly, the theoretical analysis of the algorithm is completed. Finally, the simulation is carried out in MATLAB environment to verify the feasibility of the algorithm.
引用
收藏
页码：417 / 421
页数：5
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