A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries

Min Ye, Hui Guo, Rui Xiong and Quanqing Yu

Energy, 2018, vol. 144, issue C, 789-799

Abstract: Obtaining an estimation of the parameters and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is modeled using the Thevenin model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages.

Keywords: Electric vehicles; Battery; Multi-time scales; State estimation; Dual particle filters (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:144:y:2018:i:c:p:789-799

DOI: 10.1016/j.energy.2017.12.061

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