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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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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Cites_doi	10.1007/BF01009452 10.1016/j.electacta.2017.10.153 10.1016/j.apenergy.2016.05.065 10.3390/en4111840 10.1016/j.jpowsour.2017.11.094 10.1162/106365600568220 10.1016/j.epsr.2015.10.018 10.1016/j.enconman.2012.04.014 10.1109/TVT.2007.912176 10.1002/er.4060 10.1016/j.apenergy.2016.10.020 10.1016/j.electacta.2016.12.119 10.1162/evco.1995.3.1.1 10.1007/s00521-015-1870-7 10.1016/j.apenergy.2016.01.096 10.1016/j.ipl.2010.07.026 10.1016/j.energy.2018.06.113 10.1016/j.jpowsour.2006.12.024 10.1109/TIE.2018.2838109 10.1016/j.advengsoft.2013.12.007 10.1504/IJBIC.2010.032124 10.1016/j.electacta.2017.01.057 10.1016/j.swevo.2015.07.002 10.3390/en4040582 10.1039/c2ee21892e 10.1016/j.jpowsour.2011.10.013 10.1016/j.apenergy.2016.10.059 10.1016/j.ejor.2017.02.017 10.1155/2014/105245 10.1016/j.jpowsour.2016.03.112 10.1016/j.jpowsour.2016.03.042 10.1016/j.advengsoft.2016.01.008 10.1016/j.jpowsour.2018.04.033 10.1007/s10462-016-9486-6
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References	Liu, Liu, Wang, Hu, Ma, Ren (bib11) 2016; 320 Cheng, Yao, Xing, Pecht (bib17) 2016; 9 Maniezzo, Carbonaro (bib31) 2002; 15 Fonseca (bib28) 1995; 3 Wang, Wang, Zhao, Kang, Yan, Du (bib35) 2017; 228 Zheng, Ouyang, Han, Lu, Li (bib5) 2018; 377 Zou, Zhang, Hu, Wang, Wik, Pecht (bib16) 2018; 390 Akyol, Alatas (bib26) 2017; 47 W. Gao, Y. Zheng, M. Ouyang, J. Li, X. Lai, X. Hu, IEEE Trans. Ind. Electron. Mirjalili, Mirjalili, Hatamlou (bib38) 2016; 27 K. S (bib29) 1984; 34 Zheng, Zhu, Wang, Lu, He (bib8) 2018; 158 Service (bib34) 2010; 110 Mesbahi, Khenfri, Rizoug, Chaaban, Bartholomeus, Le Moigne (bib21) 2016; 131 Holland (bib18) 2000; 8 Lim, Bastawrous, Duong, See, Zhang, Dou (bib20) 2016; 169 Kinable, Smeulders, Delcour, Spieksma (bib25) 2017; 261 Xing, Ma, Tsui, Pecht (bib1) 2011; 4 Mirjalili, Lewis (bib37) 2016; 95 Thackeray, Wolverton, Isaacs (bib2) 2012; 5 Tian, Li, Tian, Xia (bib22) 2017; 225 Zhang, Jiang, Gao, Zhang, Liu, Hu (bib19) 2017; 194 Abedinpourshotorban, Shamsuddin, Beheshti, Jawawi (bib27) 2016; 26 Kaiser (bib6) 2007; 168 Lai, Qin, Gao, Zheng, Yi (bib7) 2018; 8 Bratton, Kennedy (bib30) 2007 Lai, Zheng, Sun (bib9) 2018; 259 Xie, Ma, Bai (bib15) 2018; 42 Tian, Lu, Wang, Tao (bib32) 2014; 2014 . He, Xiong, Fan (bib10) 2011; 4 Dai, Xu, Zhu, Wei, Sun (bib24) 2016; 184 Nejad, Gladwin, Stone (bib3) 2016; 316 Hu, Li, Peng (bib14) 2012; 198 Lin, Mu, Xiong, Cao (bib13) 2017; 194 He, Xiong, Guo, Li (bib12) 2012; 64 Yang (bib33) 2010; 2 Mirjalili, Mirjalili, Lewis (bib36) 2014; 69 Szumanowski, Chang (bib4) 2008; 57 Akyol (10.1016/j.electacta.2018.11.134_bib26) 2017; 47 Lin (10.1016/j.electacta.2018.11.134_bib13) 2017; 194 Bratton (10.1016/j.electacta.2018.11.134_bib30) 2007 Lim (10.1016/j.electacta.2018.11.134_bib20) 2016; 169 Szumanowski (10.1016/j.electacta.2018.11.134_bib4) 2008; 57 Mirjalili (10.1016/j.electacta.2018.11.134_bib36) 2014; 69 Liu (10.1016/j.electacta.2018.11.134_bib11) 2016; 320 Xing (10.1016/j.electacta.2018.11.134_bib1) 2011; 4 Holland (10.1016/j.electacta.2018.11.134_bib18) 2000; 8 Zhang (10.1016/j.electacta.2018.11.134_bib19) 2017; 194 Service (10.1016/j.electacta.2018.11.134_bib34) 2010; 110 Kaiser (10.1016/j.electacta.2018.11.134_bib6) 2007; 168 Dai (10.1016/j.electacta.2018.11.134_bib24) 2016; 184 Yang (10.1016/j.electacta.2018.11.134_bib33) 2010; 2 Lai (10.1016/j.electacta.2018.11.134_bib9) 2018; 259 Wang (10.1016/j.electacta.2018.11.134_bib35) 2017; 228 Thackeray (10.1016/j.electacta.2018.11.134_bib2) 2012; 5 Tian (10.1016/j.electacta.2018.11.134_bib22) 2017; 225 Zheng (10.1016/j.electacta.2018.11.134_bib5) 2018; 377 Hu (10.1016/j.electacta.2018.11.134_bib14) 2012; 198 Lai (10.1016/j.electacta.2018.11.134_bib7) 2018; 8 He (10.1016/j.electacta.2018.11.134_bib12) 2012; 64 Maniezzo (10.1016/j.electacta.2018.11.134_bib31) 2002; 15 He (10.1016/j.electacta.2018.11.134_bib10) 2011; 4 Cheng (10.1016/j.electacta.2018.11.134_bib17) 2016; 9 10.1016/j.electacta.2018.11.134_bib23 Mirjalili (10.1016/j.electacta.2018.11.134_bib38) 2016; 27 Xie (10.1016/j.electacta.2018.11.134_bib15) 2018; 42 Mesbahi (10.1016/j.electacta.2018.11.134_bib21) 2016; 131 Zheng (10.1016/j.electacta.2018.11.134_bib8) 2018; 158 K. S (10.1016/j.electacta.2018.11.134_bib29) 1984; 34 Kinable (10.1016/j.electacta.2018.11.134_bib25) 2017; 261 Abedinpourshotorban (10.1016/j.electacta.2018.11.134_bib27) 2016; 26 Mirjalili (10.1016/j.electacta.2018.11.134_bib37) 2016; 95 Fonseca (10.1016/j.electacta.2018.11.134_bib28) 1995; 3 Tian (10.1016/j.electacta.2018.11.134_bib32) 2014; 2014 Nejad (10.1016/j.electacta.2018.11.134_bib3) 2016; 316 Zou (10.1016/j.electacta.2018.11.134_bib16) 2018; 390
References_xml	– volume: 316 start-page: 183 year: 2016 end-page: 196 ident: bib3 publication-title: J. Power Sources – volume: 320 start-page: 1 year: 2016 end-page: 12 ident: bib11 publication-title: J. Power Sources – start-page: 120 year: 2007 ident: bib30 publication-title: 2007 Ieee Swarm Intelligence Symposium – volume: 2 start-page: 78 year: 2010 end-page: 84 ident: bib33 publication-title: Int. J. Bio Inspired Comput. – volume: 64 start-page: 113 year: 2012 end-page: 121 ident: bib12 publication-title: Energy Convers. Manag. – volume: 8 start-page: 373 year: 2000 end-page: 391 ident: bib18 publication-title: Evol. Comput. – reference: W. Gao, Y. Zheng, M. Ouyang, J. Li, X. Lai, X. Hu, IEEE Trans. Ind. Electron., – volume: 3 start-page: 1 year: 1995 end-page: 16 ident: bib28 publication-title: Evol. Comput. – volume: 4 start-page: 1840 year: 2011 end-page: 1857 ident: bib1 publication-title: Energies – volume: 228 start-page: 146 year: 2017 end-page: 159 ident: bib35 publication-title: Electrochim. Acta – volume: 2014 start-page: 1 year: 2014 end-page: 10 ident: bib32 publication-title: Math. Probl Eng. – volume: 225 start-page: 225 year: 2017 end-page: 234 ident: bib22 publication-title: Electrochim. Acta – volume: 34 start-page: 975 year: 1984 end-page: 986 ident: bib29 publication-title: J. Stat. Phys. – volume: 26 start-page: 8 year: 2016 end-page: 22 ident: bib27 publication-title: Swarm Evol. Comput. – volume: 4 start-page: 582 year: 2011 end-page: 598 ident: bib10 publication-title: Energies – volume: 169 start-page: 40 year: 2016 end-page: 48 ident: bib20 publication-title: Appl. Energy – volume: 27 start-page: 495 year: 2016 end-page: 513 ident: bib38 publication-title: Neural Comput. Appl. – volume: 184 start-page: 119 year: 2016 end-page: 131 ident: bib24 publication-title: Appl. Energy – volume: 198 start-page: 359 year: 2012 end-page: 367 ident: bib14 publication-title: J. Power Sources – volume: 390 start-page: 286 year: 2018 end-page: 296 ident: bib16 publication-title: J. Power Sources – volume: 110 start-page: 917 year: 2010 end-page: 923 ident: bib34 publication-title: Inf. Process. Lett. – volume: 168 start-page: 58 year: 2007 end-page: 65 ident: bib6 publication-title: J. Power Sources – volume: 261 start-page: 475 year: 2017 end-page: 485 ident: bib25 publication-title: Eur. J. Oper. Res. – volume: 47 start-page: 417 year: 2017 end-page: 462 ident: bib26 publication-title: Artif. Intell. Rev. – volume: 259 start-page: 566 year: 2018 end-page: 577 ident: bib9 publication-title: Electrochim. Acta – volume: 131 start-page: 195 year: 2016 end-page: 204 ident: bib21 publication-title: Elec. Power Syst. Res. – volume: 57 start-page: 1425 year: 2008 end-page: 1432 ident: bib4 publication-title: IEEE Trans. Veh. Technol. – volume: 158 start-page: 1028 year: 2018 end-page: 1037 ident: bib8 publication-title: Energy – volume: 194 start-page: 569 year: 2017 end-page: 577 ident: bib19 publication-title: Appl. Energy – volume: 377 start-page: 161 year: 2018 end-page: 188 ident: bib5 publication-title: J. Power Sources – reference: . – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: bib36 publication-title: Adv. Eng. Software – volume: 42 start-page: 2710 year: 2018 end-page: 2727 ident: bib15 publication-title: Int. J. Energy Res. – volume: 8 year: 2018 ident: bib7 publication-title: Appl. Sci. Basel – volume: 15 start-page: 469 year: 2002 end-page: 492 ident: bib31 publication-title: Oper. Res. Comput. Sci. – volume: 5 start-page: 7854 year: 2012 end-page: 7863 ident: bib2 publication-title: Energy Environ. Sci. – volume: 95 start-page: 51 year: 2016 end-page: 67 ident: bib37 publication-title: Adv. Eng. Software – volume: 194 start-page: 560 year: 2017 end-page: 568 ident: bib13 publication-title: Appl. Energy – volume: 9 year: 2016 ident: bib17 publication-title: Energies – volume: 8 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib7 publication-title: Appl. Sci. Basel – volume: 9 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib17 publication-title: Energies – volume: 34 start-page: 975 year: 1984 ident: 10.1016/j.electacta.2018.11.134_bib29 publication-title: J. Stat. Phys. doi: 10.1007/BF01009452 – volume: 259 start-page: 566 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib9 publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2017.10.153 – volume: 194 start-page: 560 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib13 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.05.065 – volume: 4 start-page: 1840 year: 2011 ident: 10.1016/j.electacta.2018.11.134_bib1 publication-title: Energies doi: 10.3390/en4111840 – volume: 377 start-page: 161 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib5 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2017.11.094 – volume: 8 start-page: 373 year: 2000 ident: 10.1016/j.electacta.2018.11.134_bib18 publication-title: Evol. Comput. doi: 10.1162/106365600568220 – volume: 131 start-page: 195 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib21 publication-title: Elec. Power Syst. Res. doi: 10.1016/j.epsr.2015.10.018 – volume: 64 start-page: 113 year: 2012 ident: 10.1016/j.electacta.2018.11.134_bib12 publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2012.04.014 – volume: 57 start-page: 1425 year: 2008 ident: 10.1016/j.electacta.2018.11.134_bib4 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2007.912176 – volume: 42 start-page: 2710 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib15 publication-title: Int. J. Energy Res. doi: 10.1002/er.4060 – volume: 184 start-page: 119 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib24 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.10.020 – volume: 225 start-page: 225 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib22 publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2016.12.119 – start-page: 120 year: 2007 ident: 10.1016/j.electacta.2018.11.134_bib30 – volume: 3 start-page: 1 year: 1995 ident: 10.1016/j.electacta.2018.11.134_bib28 publication-title: Evol. Comput. doi: 10.1162/evco.1995.3.1.1 – volume: 27 start-page: 495 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib38 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-015-1870-7 – volume: 169 start-page: 40 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib20 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.01.096 – volume: 110 start-page: 917 year: 2010 ident: 10.1016/j.electacta.2018.11.134_bib34 publication-title: Inf. Process. Lett. doi: 10.1016/j.ipl.2010.07.026 – volume: 15 start-page: 469 year: 2002 ident: 10.1016/j.electacta.2018.11.134_bib31 publication-title: Oper. Res. Comput. Sci. – volume: 158 start-page: 1028 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib8 publication-title: Energy doi: 10.1016/j.energy.2018.06.113 – volume: 168 start-page: 58 year: 2007 ident: 10.1016/j.electacta.2018.11.134_bib6 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2006.12.024 – ident: 10.1016/j.electacta.2018.11.134_bib23 doi: 10.1109/TIE.2018.2838109 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.electacta.2018.11.134_bib36 publication-title: Adv. Eng. Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 2 start-page: 78 year: 2010 ident: 10.1016/j.electacta.2018.11.134_bib33 publication-title: Int. J. Bio Inspired Comput. doi: 10.1504/IJBIC.2010.032124 – volume: 228 start-page: 146 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib35 publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2017.01.057 – volume: 26 start-page: 8 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib27 publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2015.07.002 – volume: 4 start-page: 582 year: 2011 ident: 10.1016/j.electacta.2018.11.134_bib10 publication-title: Energies doi: 10.3390/en4040582 – volume: 5 start-page: 7854 year: 2012 ident: 10.1016/j.electacta.2018.11.134_bib2 publication-title: Energy Environ. Sci. doi: 10.1039/c2ee21892e – volume: 198 start-page: 359 year: 2012 ident: 10.1016/j.electacta.2018.11.134_bib14 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2011.10.013 – volume: 194 start-page: 569 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib19 publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.10.059 – volume: 261 start-page: 475 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib25 publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2017.02.017 – volume: 2014 start-page: 1 year: 2014 ident: 10.1016/j.electacta.2018.11.134_bib32 publication-title: Math. Probl Eng. doi: 10.1155/2014/105245 – volume: 320 start-page: 1 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib11 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.03.112 – volume: 316 start-page: 183 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib3 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.03.042 – volume: 95 start-page: 51 year: 2016 ident: 10.1016/j.electacta.2018.11.134_bib37 publication-title: Adv. Eng. Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 390 start-page: 286 year: 2018 ident: 10.1016/j.electacta.2018.11.134_bib16 publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.04.033 – volume: 47 start-page: 417 year: 2017 ident: 10.1016/j.electacta.2018.11.134_bib26 publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-016-9486-6
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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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