Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm
Author:
Affiliation:
1. Department of Electrical EngineeringXi'an University of TechnologyXi'anPeople's Republic of China
2. Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information ProcessingXi'anPeople's Republic of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-pel.2019.1589
Reference33 articles.
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2. Identifiability and parameter estimation of the single particle lithium‐ion battery model;Bizeray A.M.;IEEE Trans. Control Syst. Technol.,2019
3. Model‐based optimal parameter identification incorporating C‐rate, state of charge and temperature effect for advance battery management system in electric vehicles;Sangwan V.;IET Electron. Syst. Transp.,2018
4. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility;Rezvanizaniani S.M.;J. Power Sources,2014
5. The state of charge estimation of lithium‐ion batteries based on a proportional‐integral observer;Xu J.;IEEE Trans. Veh. Technol.,2014
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