Classifications of Lithium-Ion Battery Electrode Property Based on Support Vector Machine with Various Kernels

Liu, K, Yang, Z, Wang, H et al. (1 more author) (2021) Classifications of Lithium-Ion Battery Electrode Property Based on Support Vector Machine with Various Kernels. In: Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Han. 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, 30 Oct - 01 Nov 2021, Hangzhou, China. Springer, Singapore , pp. 23-34. ISBN 978-981-16-7209-5

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Copyright, Publisher and Additional Information: © Springer Nature Singapore Pte Ltd. 2021. This is an author produced version of a conference paper published in Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Hangzhou, China, October 22–24, 2021, Proceedings, Part II. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Lithium-ion battery; Battery production chain; Support vector machine; Kernel functions; Electrode property classifications
Dates:
  • Accepted: 1 June 2021
  • Published (online): 19 October 2021
  • Published: 19 October 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: 25 Oct 2021 10:22
Last Modified: 19 Oct 2022 00:16
Status: Published
Publisher: Springer, Singapore
Identification Number: https://doi.org/10.1007/978-981-16-7210-1_3

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