Recovering large-scale battery aging dataset with machine learning

Tang, X, Liu, K, Li, K orcid.org/0000-0001-6657-0522 et al. (3 more authors) (2021) Recovering large-scale battery aging dataset with machine learning. Patterns, 2. 100302. ISSN 2666-3899

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: battery aging assessment; battery aging dataset generation; lithium-ion battery management; incremental capacity analysis; model migration; machine learning; accelerated battery aging experiments
Dates:
  • Accepted: 8 June 2021
  • Published (online): 30 June 2021
  • Published: 13 August 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 02 Jul 2021 09:15
Last Modified: 02 Jul 2021 09:21
Status: Published
Publisher: Cell Press
Identification Number: https://doi.org/10.1016/j.patter.2021.100302

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