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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Veröffentlicht in:	Electrochimica acta Jg. 295; S. 1057 - 1066
Hauptverfasser:	Lai, Xin, Gao, Wenkai, Zheng, Yuejiu, Ouyang, Minggao, Li, Jianqiu, Han, Xuebing, Zhou, Long
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.02.2019 Elsevier BV
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.
Author	Ouyang, Minggao Li, Jianqiu Han, Xuebing Zhou, Long Zheng, Yuejiu Lai, Xin Gao, Wenkai
Author_xml	– sequence: 1 givenname: Xin surname: Lai fullname: Lai, Xin organization: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China – sequence: 2 givenname: Wenkai surname: Gao fullname: Gao, Wenkai organization: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China – sequence: 3 givenname: Yuejiu surname: Zheng fullname: Zheng, Yuejiu email: yuejiu.zheng@usst.edu.cn organization: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China – sequence: 4 givenname: Minggao surname: Ouyang fullname: Ouyang, Minggao organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, PR China – sequence: 5 givenname: Jianqiu surname: Li fullname: Li, Jianqiu organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, PR China – sequence: 6 givenname: Xuebing surname: Han fullname: Han, Xuebing organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, PR China – sequence: 7 givenname: Long surname: Zhou fullname: Zhou, Long organization: College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, PR China
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Keywords	Optimization algorithm Equivalent circuit model Parameter identification Metaheuristic algorithm Li-ion battery
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Snippet	A suitable model structure and matched model parameters are prerequisites for the precise estimation of the battery states. Previous studies pay little...
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SubjectTerms	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
Title	A comparative study of global optimization methods for parameter identification of different equivalent circuit models for Li-ion batteries
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