State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF

Zhang, L, Li, K orcid.org/0000-0001-6657-0522, Du, D et al. (2 more authors) (2017) State-of-Charge Estimation of Lithium Batteries Using Compact RBF Networks and AUKF. In: Li, K, Xue, Y, Cui, S, Niu, Q, Yang, Z and Luk, P, (eds.) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. LSMS 2017: International Conference on Life System Modeling and Simulation and ICSEE 2017: International Conference on Intelligent Computing for Sustainable Energy and Environment, 22-24 Sep 2017, Nanjing, China. Springer , pp. 396-405. ISBN 978-981-10-6363-3

Abstract

Metadata

Authors/Creators:
Keywords: State-of-charge (SOC); Two-stage selection (TSS); Radial basis function (RBF); Differential evolution (DE); Adaptive unscented Kalman filter (AUKF)
Dates:
  • Accepted: 1 May 2017
  • Published (online): 25 August 2017
  • Published: 25 August 2017
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: 23 Nov 2018 11:30
Last Modified: 06 Mar 2019 14:26
Status: Published
Publisher: Springer
Identification Number: https://doi.org/10.1007/978-981-10-6364-0_40

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