Data-driven prediction of Li-ion battery degradation using predicted features

Xing, W.W. orcid.org/0000-0002-3177-8478, Shah, A.A., Shah, N. et al. (6 more authors) (2023) Data-driven prediction of Li-ion battery degradation using predicted features. Processes, 11 (3). 678. ISSN 2227-9717

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Item Type: Article
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords: Li-ion battery degradation; feature engineering; Gaussian process mode; voltage and temperature curves; multi-step lookahead; end-of-life
Dates:
  • Published: March 2023
  • Published (online): 23 February 2023
  • Accepted: 29 December 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 09 Aug 2024 14:12
Last Modified: 09 Aug 2024 14:12
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: 10.3390/pr11030678
Related URLs:
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 7: Affordable and Clean Energy
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