A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries

A suitable model structure and matched model parameters are prerequisites for the precise estimation of the battery states. Previous studies pay little attention to whether a parameter identification method is suitable for a model. In this study, a comparative study is conducted by implementing mode...

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Vydané v:Electrochimica acta Ročník 295; s. 1057 - 1066
Hlavní autori: Lai, Xin, Gao, Wenkai, Zheng, Yuejiu, Ouyang, Minggao, Li, Jianqiu, Han, Xuebing, Zhou, Long
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.02.2019
Elsevier BV
Predmet:
Algorithms
Comparative studies
Equivalent circuit model
Equivalent circuits
Global optimization
Heuristic methods
Identification methods
Li-ion battery
Lithium-ion batteries
Mathematical models
Metaheuristic algorithm
Model matching
Optimization algorithm
Parameter estimation
Parameter identification
Rechargeable batteries
Optimization algorithm
Equivalent circuit model
Parameter identification
Metaheuristic algorithm
Li-ion battery
ISSN:0013-4686, 1873-3859
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Shrnutí:A suitable model structure and matched model parameters are prerequisites for the precise estimation of the battery states. Previous studies pay little attention to whether a parameter identification method is suitable for a model. In this study, a comparative study is conducted by implementing model parameter optimization for nine equivalent circuit models using nine optimizers in the entire SOC area. The following conclusions are drawn: (1) PNGV and the exact algorithms are an ideal combination in the low SOC area (0%–20%). (2) In the high SOC area (20–100%), exact algorithms are an ideal choice for the first-order RC models, and PSO is an ideal identification algorithm for second-order RC models. For the third- and fourth-order RC models, firefly algorithm has the highest accuracy with longer identification time. (3) Firefly algorithm has the superior capacity to identify the accurate model parameters and PSO has the best comprehensive performance for on-line parameter identification. •A comparative study on model parameter identification for nine models using nine optimizers in the entire SOC area.•PNGV and exact algorithms are an ideal combination in the low SOC area.•In the high SOC area, EAs and PSO are the ideal choice for the first- and second-order RC models, respectively.•For the third- and fourth-order RC models, firefly algorithm has the highest accuracy with longer identification time.•FA has excellent identification accuracy and PSO has the best comprehensive performance for on-line identification.
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content type line 14
ISSN:0013-4686
1873-3859
DOI:10.1016/j.electacta.2018.11.134