 A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filtersFeng Guo, Guangdi Hu, Shun Xiang, Pengkai Zhou, Ru Hong and Neng Xiong Energy, 2019, vol. 178, issue C, 79-88 Abstract: It is very important for the battery management system of electric vehicles to estimate the battery state of charge accurately and to achieve the on-line updating of the battery model parameters. In this paper, the estimation of the open circuit voltage is converted to the estimation of the open circuit voltage fitting parameters, the fast time-varying parameter open circuit voltage is converted into several slowly time-varying parameters. A multi-scale parameter adaptive method based on dual Kalman filters is developed. The multi-scale estimation of the battery state of charge and all parameters including open circuit voltage can be achieved. And the parameter adjustment method of dual extended Kalman filters in estimating multiple parameters is given. The experimental results show that the accuracy of the algorithm is improved by adding the estimation of the open circuit voltage. The proposed method can reduce the influence of the initial state error on the algorithm, and improve the robustness of the algorithm. Keywords: Electric vehicles; Lithium-ion batteries; State-of-Charge; Open circuit voltage; Multi-scale; Parameter adaptive estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:178:y:2019:i:c:p:79-88 DOI: 10.1016/j.energy.2019.04.126 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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