A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter

Abstract An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation appro...

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Autores principales:	Shichun Yang, Sida Zhou, Yang Hua, Xinan Zhou, Xinhua Liu, Yuwei Pan, Heping Ling, Billy Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c8b762e012e94c5295ec00d15b98c844
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spelling	oai:doaj.org-article:c8b762e012e94c5295ec00d15b98c8442021-12-02T13:30:51ZA parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter10.1038/s41598-021-84729-12045-2322https://doaj.org/article/c8b762e012e94c5295ec00d15b98c8442021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84729-1https://doaj.org/toc/2045-2322Abstract An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.Shichun YangSida ZhouYang HuaXinan ZhouXinhua LiuYuwei PanHeping LingBilly WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shichun Yang Sida Zhou Yang Hua Xinan Zhou Xinhua Liu Yuwei Pan Heping Ling Billy Wu A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
description	Abstract An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.
format	article
author	Shichun Yang Sida Zhou Yang Hua Xinan Zhou Xinhua Liu Yuwei Pan Heping Ling Billy Wu
author_facet	Shichun Yang Sida Zhou Yang Hua Xinan Zhou Xinhua Liu Yuwei Pan Heping Ling Billy Wu
author_sort	Shichun Yang
title	A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
title_short	A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
title_full	A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
title_fullStr	A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
title_full_unstemmed	A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter
title_sort	parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended kalman filter
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c8b762e012e94c5295ec00d15b98c844
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A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter

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