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Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise
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Du, Xinghao, Meng, Jinhao, Liu, Kailong, Zhang, Yingmin, Wang, Shunli, Peng, Jichang and Liu, Tianqi (2023) Online identification of lithium-ion battery model parameters with initial value uncertainty and measurement noise. Chinese Journal of Mechanical Engineering, 36 (1). 7. doi:10.1186/s10033-023-00846-0 ISSN 1000-9345.
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Official URL: http://doi.org/10.1186/s10033-023-00846-0
Abstract
Online parameter identification is essential for the accuracy of the battery equivalent circuit model (ECM). The traditional recursive least squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the recursive total least squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, a fast convergence rate, and also robustness against noise corruption.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
Library of Congress Subject Headings (LCSH): | Battery management systems, Lithium ion batteries -- Mathematical models, Electric circuits, Equivalent, Least squares | |||||||||
Journal or Publication Title: | Chinese Journal of Mechanical Engineering | |||||||||
Publisher: | Springer | |||||||||
ISSN: | 1000-9345 | |||||||||
Official Date: | 20 January 2023 | |||||||||
Dates: |
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Volume: | 36 | |||||||||
Number: | 1 | |||||||||
Article Number: | 7 | |||||||||
DOI: | 10.1186/s10033-023-00846-0 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 28 February 2023 | |||||||||
Date of first compliant Open Access: | 28 February 2023 | |||||||||
RIOXX Funder/Project Grant: |
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