An unscented kalman filtering method for estimation of state-of-charge of lithium-ion battery

被引：8

作者：

Guo, Jishu ^{[1
]}

Liu, Shulin ^{[2
]}

Zhu, Rui ^{[3
]}

机构：

[1] Qilu Univ Technol, Shandong Acad Sci, Sch Informat & Automat Engn, Jinan, Peoples R China

[2] Linyi Univ, Sch Automat & Elect Engn, Linyi, Peoples R China

[3] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China

来源：

FRONTIERS IN ENERGY RESEARCH | 2023年 / 10卷

关键词：

EKF; UKF; SOC estimation; lithium-ion battery; model; SOC ESTIMATION;

D O I：

10.3389/fenrg.2022.998002

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Accurate estimation of battery state of charge (SOC) is of great significance to improve battery management and service life. An unscented Kalman filter (UKF) method is used to increase the accuracy of SOC estimation in this paper. Firstly, a battery model that the parameters are identified by using the least squares algorithm is established, which is foundation of the two-order RC equivalent circuit model. Secondly, SOC is estimated by UKF. In order to validate the method, experiments have been carried out under different operating conditions for LiFePO4 batteries. The obtained results are compared with that of the extended Kalman filter. Finally, the comparison shows that the UKF method provides better accuracy in the battery SOC estimation. Its estimation error is less than 2%, which is better than EKF algorithm. An effective method is provided for state estimation for battery management system.

引用

页数：9

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