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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 (2019) Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Transactions on Industrial Informatics . doi:10.1109/TII.2019.2941747 (In Press)
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WRAP-Gaussian-process-regression-automatic-kernal-batteries-Widanage-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: http://doi.org/10.1109/TII.2019.2941747
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 | ||||||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Engineering Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) |
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| 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: | 2 October 2019 | ||||||||||||
| Dates: |
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| Date of first compliant deposit: | 28 November 2019 | ||||||||||||
| DOI: | 10.1109/TII.2019.2941747 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | In Press | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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