Fast battery capacity estimation using convolutional neural networks

Li, Y orcid.org/0000-0002-5521-9224, Li, K orcid.org/0000-0001-6657-0522, Liu, X orcid.org/0000-0001-6354-2067 et al. (1 more author) (2020) Fast battery capacity estimation using convolutional neural networks. Transactions of the Institute of Measurement and Control. ISSN 0142-3312

Abstract

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Copyright, Publisher and Additional Information: © The Author(s) 2020. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
Keywords: Battery capacity estimation, convolutional neural networks, lithium-ion batteries, machine learning
Dates:
  • Accepted: 17 September 2020
  • Published (online): 5 November 2020
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)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/R030243/1
Depositing User: Symplectic Publications
Date Deposited: 06 Oct 2020 12:35
Last Modified: 31 Dec 2020 07:22
Status: Published online
Publisher: SAGE
Identification Number: https://doi.org/10.1177/0142331220966425

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