State of Charge Estimation of Lithium-Ion Batteries Based on Maximum Correlation-Entropy Criterion Extended Kalman Filtering Algorithm

被引：0

作者：

Wu C. ^{[1
]}

Hu W. ^{[1
]}

Meng J. ^{[2
]}

Liu Z. ^{[1
]}

Cheng Y. ^{[1
]}

机构：

[1] School of Electronics and Control Engineering, Chang'an University, Xi'an

[2] College of Electrical Engineering, Sichuan University, Chengdu

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2021年 / 36卷 / 24期

关键词：

Lithium-ion battery; Non-Gaussian noise; Parameter identification; State of charge;

D O I：

10.19595/j.cnki.1000-6753.tces.210950

中图分类号：

学科分类号：

摘要：

The traditional extended Kalman filter(EKF)algorithm has low accuracy in estimating the state of charge(SOC)of lithium-ion battery under the non-Gaussian noise interference. Therefore, a new extended Kalman filter (MCC-EKF) algorithm based on maximum correlation-entropy criterion was proposed. Firstly, the Thevenin equivalent circuit of the lithium-ion battery was model and its parameters was identified. Secondly, the proposed algorithm MCC-EKF and EKF algorithm were used to estimate the SOC under different noise interference. The experimental results show that, compared with the EKF algorithm, the running time of the new algorithm increases by 0.282s and the estimation accuracy increases by 19% under Gaussian noise interference; under non-Gaussian noise interference, the running time of the new algorithm increases by 0.418s and the estimation accuracy increases by 51%. In addition, given the wrong initial SOC value, the new algorithm can converge to the true value within 10s after the battery starts working, indicating that the new algorithm has better robustness. The proposed algorithm has high estimation accuracy and good robustness while the increase of running time is small, and it is an effective SOC estimation method. © 2021, Electrical Technology Press Co. Ltd. All right reserved.

引用

页码：5165 / 5175

页数：10

共 22 条

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