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Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries

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Liu, Kailong, Li, Yi, Hu, Xiaosong, Lucu, Mattin and Widanage, Widanalage Dhammika (2020) Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Transactions on Industrial Informatics, 16 (6). pp. 3767-3777. doi:10.1109/TII.2019.2941747 ISSN 1551-3203.

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Official URL: http://doi.org/10.1109/TII.2019.2941747

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Abstract

Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regression (GPR) technique is employed to capture the underlying mapping among capacity, storage temperature, and SOC. By modifying the isotropic kernel function with an automatic relevance determination (ARD) structure, high relevant input features can be effectively extracted to improve prediction accuracy and robustness. Experimental battery calendar aging data from nine storage cases are utilized for model training, validation, and comparison, which is more meaningful and practical than using the data from a single condition. Illustrative results demonstrate that the proposed GPR model with ARD Matern32 (M32) kernel outperforms other counterparts and can achieve reliable prediction results for all storage cases. Even for the partial-data training test, multi-step prediction test and accelerated aging training test, the proposed ARD-based GPR model is still capable of excavating the useful features, therefore offering good generalization ability and accurate prediction results for calendar aging under various storage conditions. This is the first known data-driven application that utilizes the GPR with ARD kernel to perform battery calendar aging prognosis.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Lithium ion batteries , Electric vehicles -- Batteries -- Testing, Gaussian processes
Journal or Publication Title: IEEE Transactions on Industrial Informatics
Publisher: IEEE
ISSN: 1551-3203
Official Date: June 2020
Dates:
DateEvent
June 2020Published
2 October 2019Available
30 August 2019Accepted
Volume: 16
Number: 6
Page Range: pp. 3767-3777
DOI: 10.1109/TII.2019.2941747
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 28 November 2019
Date of first compliant Open Access: 28 November 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
685716[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
UNSPECIFIEDInnovate UKhttp://dx.doi.org/10.13039/501100006041
UNSPECIFIEDJaguar Land Rover (Firm)http://viaf.org/viaf/305209406

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