A sparse least squares support vector machine used for SOC estimation of Li-ion Batteries

Zhang, L, Li, K orcid.org/0000-0001-6657-0522, Du, D et al. (2 more authors) (2019) A sparse least squares support vector machine used for SOC estimation of Li-ion Batteries. In: IFAC-PapersOnLine. 5th IFAC Conference on Intelligent Control and Automation Sciences ICONS 2019, 21-23 Aug 2019, Belfast, UK. Elsevier , pp. 256-261.

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons CC-BY-NC-ND license
Keywords: state-of-charge (SOC); least squares support vector machine (LS-SVM); unscented Kalman filter (UKF)
Dates:
  • Published: September 2019
  • Accepted: 16 May 2019
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: 03 Jan 2020 09:58
Last Modified: 07 Jan 2020 10:14
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.ifacol.2019.09.150

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