A noise‐immune model identification method for lithium‐ion battery using two‐swarm cooperative particle swarm optimization algorithm based on adaptive dynamic sliding window

International Journal of Energy ResearchVolume 46, Issue 3 p. 3512-3528 RESEARCH ARTICLE A noise-immune model identification method for lithium-ion battery using two-swarm cooperative particle swarm optimization algorithm based on adaptive dynamic sliding window Yongjie Zhu, Yongjie Zhu orcid.org/0000-0002-9998-1659 School of Electric Engineering, Shanghai University of Electric Power, Shanghai, ChinaSearch for more papers by this authorJiajun Chen, Corresponding Author Jiajun Chen [email protected] Pegasus Power Energy Co., Ltd., Hangzhou, China Correspondence Jiajun Chen, Pegasus Power Energy Co., Ltd., Hangzhou 310019, China. Email: [email protected] Ling Mao, School of Electric Engineering, Shanghai University of Electric Power, Shanghai 200090, China. Email: [email protected]Search for more papers by this authorLing Mao, Corresponding Author Ling Mao [email protected] School of Electric Engineering, Shanghai University of Electric Power, Shanghai, China Correspondence Jiajun Chen, Pegasus Power Energy Co., Ltd., Hangzhou 310019, China. Email: [email protected] Ling Mao, School of Electric Engineering, Shanghai University of Electric Power, Shanghai 200090, China. Email: [email protected]Search for more papers by this authorJinbin Zhao, Jinbin Zhao School of Electric Engineering, Shanghai University of Electric Power, Shanghai, ChinaSearch for more papers by this author Yongjie Zhu, Yongjie Zhu orcid.org/0000-0002-9998-1659 School of Electric Engineering, Shanghai University of Electric Power, Shanghai, ChinaSearch for more papers by this authorJiajun Chen, Corresponding Author Jiajun Chen [email protected] Pegasus Power Energy Co., Ltd., Hangzhou, China Correspondence Jiajun Chen, Pegasus Power Energy Co., Ltd., Hangzhou 310019, China. Email: [email protected] Ling Mao, School of Electric Engineering, Shanghai University of Electric Power, Shanghai 200090, China. Email: [email protected]Search for more papers by this authorLing Mao, Corresponding Author Ling Mao [email protected] School of Electric Engineering, Shanghai University of Electric Power, Shanghai, China Correspondence Jiajun Chen, Pegasus Power Energy Co., Ltd., Hangzhou 310019, China. Email: [email protected] Ling Mao, School of Electric Engineering, Shanghai University of Electric Power, Shanghai 200090, China. Email: [email protected]Search for more papers by this authorJinbin Zhao, Jinbin Zhao School of Electric Engineering, Shanghai University of Electric Power, Shanghai, ChinaSearch for more papers by this author First published: 05 November 2021 https://doi.org/10.1002/er.7401 Funding information: National Natural Science Foundation of China, Grant/Award Number: 51777120 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Summary Accurate and reliable model parameters are not only a prerequisite for model-based estimation but also a significant part of battery operating characteristics. However, the measurement signal inevitably contains noise, which brings great challenges to model identification. This paper focuses on the noise immunity performance of model identification based on two-swarm cooperative particle swarm optimization. An adaptive dynamic sliding window based on the current rate criterion and the identification results feedback is designed to avoid data redundancy and improve the robustness of model identification. The model parameters are obtained using two-swarm cooperative particle swarm optimization based on the adaptive dynamic sliding window. The proposed method effectively improves the accuracy and speed of parameter identification through optimization of data fragments and particle update rules. Compared with two existing parameter identification methods, simulation studies illustrate that the average mean square deviation of the proposed method is reduced by at least 35 dB. The proposed method is superior to existing parameter identification methods in noise immunity performance, parameter identification reliability, and state-of-charge estimation accuracy. By employing the proposed method, the maximum errors of state-of-charge estimation are limited within 1% under experimental verification. The experiment results verify that the proposed method has the potential to extract reliable model features online. Volume46, Issue310 March 2022Pages 3512-3528 RelatedInformation