Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data
The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which...
Uloženo v:
| Vydáno v: | Energy reports Ročník 11; s. 2049 - 2058 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.06.2024
Elsevier |
| Témata: | |
| ISSN: | 2352-4847, 2352-4847 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10−3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10−3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. |
|---|---|
| AbstractList | The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10 -3 , and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10 -3 . In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10−3, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10−3. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency. |
| Author | Al-Dhaifallah, Mujahed Ghadbane, Houssam Eddine Ferahtia, Seydali Rezk, Hegazy Barkat, Said |
| Author_xml | – sequence: 1 givenname: Houssam Eddine surname: Ghadbane fullname: Ghadbane, Houssam Eddine organization: Electrical Engineering Laboratory of Guelma (LGEG), Electrotechnical and Automatic Engineering Department, Université 8 Mai 1945, Guelma 24000, Algeria – sequence: 2 givenname: Hegazy surname: Rezk fullname: Rezk, Hegazy organization: Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia – sequence: 3 givenname: Seydali surname: Ferahtia fullname: Ferahtia, Seydali organization: University of Nantes, IREENA Laboratory, Industrial Engineering Department, France – sequence: 4 givenname: Said surname: Barkat fullname: Barkat, Said organization: University of M’sila, Electrical Engineering Laboratory, Electrical Engineering Departement, Algeria – sequence: 5 givenname: Mujahed surname: Al-Dhaifallah fullname: Al-Dhaifallah, Mujahed email: mujahed@kfupm.edu.sa organization: Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia |
| BackLink | https://nantes-universite.hal.science/hal-05082174$$DView record in HAL |
| BookMark | eNp9kEtr3DAQgEVIIY_mD_Skaw929PLahl5CaJrAQi7JWcjSKDuL1jaSsrD0z1fulhJy2NMMM_PNMN8VOR-nEQj5xlnNGV_dbmt4O8RaMKFqxmvWyjNyKWQjKtWp9vxDfkFuUtoyxngvmFrJS_L7ec64M4HOJpodZIgUHYwZPVqTcRppytHkcoCaeQ4IjuaJBswbfN9VS38wuVAHupscBOqnSCGAzREt3cMGbYBE3xOOb9RF3AO1h1KizmTzlXzxJiS4-RevyevDz5f7x2r9_Ovp_m5dWdl2uVr10jVtD1a5fpCc-VaseMehM1KqXhnoBi9FO3TOt6AG03jGjHJeWNPYBqS8Jt-Pezcm6DmWd-NBTwb1491aLzXWsE7wVu15mRXHWRunlCL4_wBnerGtt3qxrRfbmnFdbBeo-wRZzH_tFXcYTqM_jigUAXuEqJNFGC04jMWidhOewv8AWwif1Q |
| CitedBy_id | crossref_primary_10_3390_mca29040052 crossref_primary_10_1016_j_rineng_2024_102845 crossref_primary_10_1007_s00202_025_03213_5 crossref_primary_10_3390_asi7050075 crossref_primary_10_1515_mt_2025_0291 crossref_primary_10_1016_j_est_2025_117389 crossref_primary_10_1155_er_8883900 crossref_primary_10_1016_j_measurement_2025_118449 crossref_primary_10_3390_math13091504 crossref_primary_10_1016_j_compchemeng_2024_108894 crossref_primary_10_1149_1945_7111_adeece crossref_primary_10_55525_tjst_1437348 crossref_primary_10_1007_s11581_025_06578_6 crossref_primary_10_1016_j_egyr_2024_04_039 crossref_primary_10_3390_en17194988 |
| Cites_doi | 10.1016/j.egyr.2022.02.026 10.1016/j.electacta.2019.03.199 10.1109/ACCESS.2017.2678598 10.1007/s00521-023-08261-1 10.1016/j.engappai.2017.06.029 10.1016/j.epsr.2015.10.018 10.1002/er.6614 10.1049/iet-pel.2019.1589 10.1016/j.isatra.2019.08.004 10.1016/j.cma.2020.113609 10.1109/TVT.2010.2103333 10.1109/TEC.2006.874229 10.1109/TII.2014.2299237 10.1016/j.est.2021.103485 10.1016/j.egyr.2021.10.086 10.1109/TPEL.2012.2210564 10.3389/fmech.2022.1126450 10.1016/j.egyr.2023.09.082 10.1002/er.6921 10.1109/ACCESS.2022.3172789 10.1016/j.engappai.2022.105075 10.1016/j.jpowsour.2019.227575 10.1016/j.egyr.2021.07.029 10.1016/j.egyr.2021.08.182 10.1016/j.est.2021.103848 10.1002/er.7834 10.1016/j.isatra.2022.08.025 10.1016/j.egyr.2023.01.109 10.1109/ACCESS.2020.3015919 10.1016/j.egyr.2023.01.018 10.1016/j.enconman.2020.112595 10.1109/ACCESS.2021.3133286 10.1002/er.6315 10.1016/j.egyr.2022.10.059 10.1016/j.engappai.2012.09.013 10.1016/j.ensm.2020.11.026 10.1016/j.neucom.2023.02.010 10.1016/j.electacta.2018.08.076 10.1016/j.egyr.2023.03.091 10.1016/j.egyr.2021.06.051 10.1016/j.energy.2018.09.101 |
| ContentType | Journal Article |
| Copyright | 2024 The Authors licence_http://creativecommons.org/publicdomain/zero |
| Copyright_xml | – notice: 2024 The Authors – notice: licence_http://creativecommons.org/publicdomain/zero |
| DBID | 6I. AAFTH AAYXX CITATION 1XC VOOES |
| DOI | 10.1016/j.egyr.2024.01.073 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2352-4847 |
| EndPage | 2058 |
| ExternalDocumentID | oai:HAL:hal-05082174v1 10_1016_j_egyr_2024_01_073 S2352484724000738 |
| GroupedDBID | 0R~ 0SF 4.4 457 5VS 6I. AACTN AAEDT AAEDW AAFTH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE ADVLN AEXQZ AFJKZ AFTJW AGHFR AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB GROUPED_DOAJ KQ8 M41 M~E NCXOZ O9- OK1 ROL SSZ AAYWO AAYXX ACVFH ADCNI AEUPX AFPUW AIGII AKBMS AKYEP APXCP CITATION 1XC VOOES |
| ID | FETCH-LOGICAL-c378t-693d579ec4d9b310f726181e8a33494ae8bf327b8df7e4ba5f00a4df2ca5c5e33 |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001174787400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2352-4847 |
| IngestDate | Sat Nov 15 06:26:14 EST 2025 Sat Nov 29 01:39:08 EST 2025 Tue Nov 18 21:53:30 EST 2025 Sat Oct 19 15:53:56 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Metaheuristic optimization algorithms Electric vehicles Li-ion battery Parameters identification Electric vehicles, Li-ion battery, Metaheuristic optimization algorithms, Parameters identification |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. licence_http://creativecommons.org/publicdomain/zero/: http://creativecommons.org/publicdomain/zero |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c378t-693d579ec4d9b310f726181e8a33494ae8bf327b8df7e4ba5f00a4df2ca5c5e33 |
| OpenAccessLink | http://dx.doi.org/10.1016/j.egyr.2024.01.073 |
| PageCount | 10 |
| ParticipantIDs | hal_primary_oai_HAL_hal_05082174v1 crossref_primary_10_1016_j_egyr_2024_01_073 crossref_citationtrail_10_1016_j_egyr_2024_01_073 elsevier_sciencedirect_doi_10_1016_j_egyr_2024_01_073 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-06-01 |
| PublicationDateYYYYMMDD | 2024-06-01 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Energy reports |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd Elsevier |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier |
| References | Hu (bib21) 2022; vol. 8 Bhide, Shim (bib5) 2011; vol. 60 Zhang, Shang, Li, Cui, Duan, Zhang (bib43) 2020; vol. 97 Houssein, Hashim, Ferahtia, Rezk (bib20) 2022; vol. 46 Pang, Mou, Guo, Zhang (bib27) 2019; vol. 307 Chen, Rincón-Mora (bib7) 2006; vol. 21 Shehadeh (bib32) 2023; vol. 35 Lai (bib25) 2021; vol. 35 Kumar, Saikat, Dilip, Pratihar (bib23) 2023 Andre, Nuhic, Soczka-Guth, Sauer (bib2) 2013; vol. 26 El Marghichi (bib13) 2023; vol. 10 Shen, Li (bib33) 2017; vol. 5 Dusmez, Khaligh (bib11) 2014; vol. 10 Trojovska, Dehghani, Trojovsky (bib38) 2022; vol. 10 Babu, Vasudevan, Ramachandaramurthy, Sani, Chemud, Lajim (bib4) 2020; vol. 8 Ferahtia, Djeroui, Rezk, Chouder, Houari, Machmoum (bib16) 2021; vol. 45 Sánchez, Couso, Blanco (bib30) 2017; vol. 64 Yang (bib41) 2020; vol. 208 Su (bib35) 2023; vol. 532 Assaad, El-sehiemy, Ginidi, Elattar, Shaheen (bib3) 2022; vol. 51 Sánchez, Couso, Blanco (bib29) 2017; vol. 64 Gao (bib18) 2023; vol. 9 Wang, Jin, Bai, Fan, Shi, Fernandez (bib40) 2021; vol. 7 Abualigah, Diabat, Mirjalili, Abd Elaziz, Gandomi (bib1) 2021; vol. 376 Zhu (bib45) 2020; vol. 448 Wang, Kang, Tan, Luo (bib39) 2018; vol. 289 Tian, Li, Liu, Yang, Wang, Chang (bib36) 2022; vol. 8 Dehghani, Hubalovsky, Trojovsky (bib10) 2021; vol. 9 Einhorn, Conte, Kral, Fleig (bib12) 2013; vol. 28 Tremblay, Dessaint (bib37) 2009; vol. 3 Zazoum (bib42) 2023; vol. 9 Guo, Yang, Han, Feng, Lu, Ouyang (bib19) 2021; vol. 45 Chen, Xiao, Yan, Guo (bib6) 2021; vol. 7 Ferahtia, Rezk, Djerioui, Houari, Motahhir, Zeghlache (bib17) 2023; vol. 134 Shaheen, Hamida, El-Sehiemy, Elattar (bib31) 2021; vol. 7 Hu, Li, Li, Fu, Qin, Li (bib22) 2018; vol. 165 Mesbahi, Khenfri, Rizoug, Chaaban, Bartholomeüs, Le Moigne (bib26) 2016; vol. 131 Chen, Li, Sun, Zhao, Guo (bib8) 2023; vol. 9 Zhao, Zhang, Ma, Chen (bib44) 2022; vol. 114 Fathy, Ferahtia, Rezk, Yousri, Abdelkareem, Olabi (bib15) 2022; vol. 46 Lai (bib24) 2021; vol. 45 Ren, Xie, Sun, Zhang, Yan (bib28) 2020; vol. 13 Shi, Guo, Chen (bib34) 2021; vol. 44 Dehghani, Trojovský (bib9) 2023; vol. 8 Essiet, Sun (bib14) 2021; vol. 7 Houssein (10.1016/j.egyr.2024.01.073_bib20) 2022; vol. 46 Hu (10.1016/j.egyr.2024.01.073_bib21) 2022; vol. 8 Shaheen (10.1016/j.egyr.2024.01.073_bib31) 2021; vol. 7 Zhao (10.1016/j.egyr.2024.01.073_bib44) 2022; vol. 114 Assaad (10.1016/j.egyr.2024.01.073_bib3) 2022; vol. 51 Andre (10.1016/j.egyr.2024.01.073_bib2) 2013; vol. 26 Pang (10.1016/j.egyr.2024.01.073_bib27) 2019; vol. 307 Shehadeh (10.1016/j.egyr.2024.01.073_bib32) 2023; vol. 35 Su (10.1016/j.egyr.2024.01.073_bib35) 2023; vol. 532 Trojovska (10.1016/j.egyr.2024.01.073_bib38) 2022; vol. 10 Chen (10.1016/j.egyr.2024.01.073_bib7) 2006; vol. 21 Dehghani (10.1016/j.egyr.2024.01.073_bib10) 2021; vol. 9 Bhide (10.1016/j.egyr.2024.01.073_bib5) 2011; vol. 60 Ren (10.1016/j.egyr.2024.01.073_bib28) 2020; vol. 13 Zhang (10.1016/j.egyr.2024.01.073_bib43) 2020; vol. 97 Ferahtia (10.1016/j.egyr.2024.01.073_bib16) 2021; vol. 45 Dusmez (10.1016/j.egyr.2024.01.073_bib11) 2014; vol. 10 Shen (10.1016/j.egyr.2024.01.073_bib33) 2017; vol. 5 Guo (10.1016/j.egyr.2024.01.073_bib19) 2021; vol. 45 Lai (10.1016/j.egyr.2024.01.073_bib25) 2021; vol. 35 Einhorn (10.1016/j.egyr.2024.01.073_bib12) 2013; vol. 28 Wang (10.1016/j.egyr.2024.01.073_bib40) 2021; vol. 7 El Marghichi (10.1016/j.egyr.2024.01.073_bib13) 2023; vol. 10 Zhu (10.1016/j.egyr.2024.01.073_bib45) 2020; vol. 448 Babu (10.1016/j.egyr.2024.01.073_bib4) 2020; vol. 8 Yang (10.1016/j.egyr.2024.01.073_bib41) 2020; vol. 208 Fathy (10.1016/j.egyr.2024.01.073_bib15) 2022; vol. 46 Ferahtia (10.1016/j.egyr.2024.01.073_bib17) 2023; vol. 134 Dehghani (10.1016/j.egyr.2024.01.073_bib9) 2023; vol. 8 Lai (10.1016/j.egyr.2024.01.073_bib24) 2021; vol. 45 Sánchez (10.1016/j.egyr.2024.01.073_bib30) 2017; vol. 64 Zazoum (10.1016/j.egyr.2024.01.073_bib42) 2023; vol. 9 Shi (10.1016/j.egyr.2024.01.073_bib34) 2021; vol. 44 Chen (10.1016/j.egyr.2024.01.073_bib8) 2023; vol. 9 Kumar (10.1016/j.egyr.2024.01.073_bib23) 2023 Mesbahi (10.1016/j.egyr.2024.01.073_bib26) 2016; vol. 131 Tian (10.1016/j.egyr.2024.01.073_bib36) 2022; vol. 8 Wang (10.1016/j.egyr.2024.01.073_bib39) 2018; vol. 289 Abualigah (10.1016/j.egyr.2024.01.073_bib1) 2021; vol. 376 Hu (10.1016/j.egyr.2024.01.073_bib22) 2018; vol. 165 Sánchez (10.1016/j.egyr.2024.01.073_bib29) 2017; vol. 64 Essiet (10.1016/j.egyr.2024.01.073_bib14) 2021; vol. 7 Chen (10.1016/j.egyr.2024.01.073_bib6) 2021; vol. 7 Gao (10.1016/j.egyr.2024.01.073_bib18) 2023; vol. 9 Tremblay (10.1016/j.egyr.2024.01.073_bib37) 2009; vol. 3 |
| References_xml | – volume: vol. 208 year: 2020 ident: bib41 article-title: Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification publication-title: Energy Convers. Manag. – volume: vol. 45 start-page: 16741 year: 2021 end-page: 16753 ident: bib16 article-title: Optimal parameter identification strategy applied to lithium-ion battery model publication-title: Int. J. Energy Res. – volume: vol. 64 start-page: 367 year: 2017 end-page: 377 ident: bib29 article-title: Engineering applications of artificial intelligence A class of Monotone Fuzzy rule-based Wiener systems with an application to Li-ion battery modelling publication-title: Eng. Appl. Artif. Intell. – volume: vol. 9 start-page: 1152 year: 2023 end-page: 1158 ident: bib42 article-title: Lithium-ion battery state of charge prediction based on machine learning approach publication-title: Energy Rep. – volume: vol. 114 year: 2022 ident: bib44 article-title: Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications publication-title: Eng. Appl. Artif. Intell. – volume: vol. 10 start-page: 1960 year: 2014 end-page: 1971 ident: bib11 article-title: A supervisory power-splitting approach for a new ultracapacitor-battery vehicle deploying two propulsion machines publication-title: IEEE Trans. Ind. Inform. – volume: vol. 64 start-page: 367 year: 2017 end-page: 377 ident: bib30 article-title: A class of Monotone Fuzzy rule-based Wiener systems with an application to Li-ion battery modelling publication-title: Eng. Appl. Artif. Intell. – volume: vol. 44 year: 2021 ident: bib34 article-title: Parameter identification method for lithium-ion batteries based on recursive least square with sliding window difference forgetting factor publication-title: J. Energy Storage – volume: vol. 448 year: 2020 ident: bib45 article-title: Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance publication-title: J. Power Sources – volume: vol. 35 start-page: 10733 year: 2023 end-page: 10749 ident: bib32 article-title: Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization publication-title: Neural Comput. Appl. – volume: vol. 7 start-page: 320 year: 2021 end-page: 329 ident: bib6 article-title: A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects publication-title: Energy Rep. – volume: vol. 97 start-page: 448 year: 2020 end-page: 457 ident: bib43 article-title: A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries publication-title: ISA Trans. – volume: vol. 46 start-page: 10564 year: 2022 end-page: 10575 ident: bib15 article-title: Robust parameter estimation approach of Lithium-ion batteries employing bald eagle search algorithm publication-title: Int. J. Energy Res. – volume: vol. 532 start-page: 183 year: 2023 end-page: 214 ident: bib35 article-title: RIME: a physics-based optimization publication-title: Neurocomputing – volume: vol. 3 start-page: 289 year: 2009 end-page: 298 ident: bib37 article-title: Experimental validation of a battery dynamic model for EV applications publication-title: World Electr. Veh. J. 2009 – volume: vol. 307 start-page: 474 year: 2019 end-page: 487 ident: bib27 article-title: Electrochimica acta parameter identi fi cation and systematic validation of an enhanced single-particle model with aging degradation physics for Li-ion batteries publication-title: Electrochim. Acta – volume: vol. 10 start-page: 49445 year: 2022 end-page: 49473 ident: bib38 article-title: Zebra optimization algorithm: a new bio-inspired optimization algorithm for solving optimization algorithm publication-title: IEEE Access – volume: vol. 9 start-page: 1937 year: 2023 end-page: 1947 ident: bib8 article-title: SOC estimation of retired lithium-ion batteries for electric vehicle with improved particle filter by H-infinity filter publication-title: Energy Rep. – volume: vol. 8 year: 2023 ident: bib9 article-title: Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems publication-title: Front. Mech. Eng. – volume: vol. 134 start-page: 357 year: 2023 end-page: 379 ident: bib17 article-title: Modified bald eagle search algorithm for lithium-ion battery model parameters extraction publication-title: ISA Trans. – volume: vol. 289 start-page: 376 year: 2018 end-page: 388 ident: bib39 article-title: Electrochimica acta an online method to simultaneously identify the parameters and estimate states for lithium ion batteries publication-title: Electrochim. Acta – volume: vol. 60 start-page: 819 year: 2011 end-page: 829 ident: bib5 article-title: Novel predictive electric Li-ion battery model incorporating thermal and rate factor effects publication-title: IEEE Trans. Veh. Technol. – volume: vol. 28 start-page: 1429 year: 2013 end-page: 1437 ident: bib12 article-title: Comparison, selection, and parameterization of electrical battery models for automotive applications publication-title: IEEE Trans. Power Electron – volume: vol. 7 start-page: 5562 year: 2021 end-page: 5574 ident: bib40 article-title: A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries publication-title: Energy Rep. – volume: vol. 26 start-page: 951 year: 2013 end-page: 961 ident: bib2 article-title: Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles publication-title: Eng. Appl. Artif. Intell. – volume: vol. 46 year: 2022 ident: bib20 article-title: Battery parameter identification strategy based on modified coot optimization algorithm publication-title: J. Energy Storage – volume: vol. 376 year: 2021 ident: bib1 article-title: The arithmetic optimization algorithm publication-title: Comput. Methods Appl. Mech. Eng. – volume: vol. 8 start-page: 13723 year: 2022 end-page: 13734 ident: bib36 article-title: Lithium-ion battery charging optimization based on electrical, thermal and aging mechanism models publication-title: Energy Rep. – volume: vol. 8 start-page: 774 year: 2022 end-page: 784 ident: bib21 article-title: Performance evaluation strategy for battery pack of electric vehicles: online estimation and offline evaluation publication-title: Energy Rep. – volume: vol. 10 start-page: 2710 year: 2023 end-page: 2724 ident: bib13 article-title: Enhancing battery capacity estimation accuracy using the bald eagle search algorithm publication-title: Energy Rep. – volume: vol. 45 start-page: 12825 year: 2021 end-page: 12837 ident: bib19 article-title: Parameter identification of fractional-order model with transfer learning for aging lithium-ion batteries publication-title: Int. J. Energy Res. – volume: vol. 8 start-page: 148702 year: 2020 end-page: 148721 ident: bib4 article-title: A comprehensive review of hybrid energy storage systems: converter topologies, control strategies and future prospects publication-title: IEEE Access – volume: vol. 131 start-page: 195 year: 2016 end-page: 204 ident: bib26 article-title: Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm publication-title: Electr. Power Syst. Res. – volume: vol. 165 start-page: 153 year: 2018 end-page: 163 ident: bib22 article-title: Lithium-ion battery modeling and parameter identification based on fractional theory publication-title: Energy – volume: vol. 45 start-page: 7326 year: 2021 end-page: 7340 ident: bib24 article-title: Online internal short circuit detection method considering equalization electric quantity for lithium-ion battery pack in electric vehicles publication-title: Int. J. Energy Res. – year: 2023 ident: bib23 article-title: An improved design of knee orthosis using self ‑ adaptive bonobo optimizer ( SaBO) publication-title: J. Intell. Robot. Syst. – volume: vol. 7 start-page: 7170 year: 2021 end-page: 7185 ident: bib31 article-title: Optimal parameter identification of linear and non-linear models for Li-Ion Battery Cells publication-title: Energy Rep. – volume: vol. 7 start-page: 4348 year: 2021 end-page: 4359 ident: bib14 article-title: Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services publication-title: Energy Rep. – volume: vol. 5 start-page: 4377 year: 2017 end-page: 4387 ident: bib33 article-title: A sensitivity-based group-wise parameter identification algorithm for the electric model of Li-ion battery publication-title: IEEE Access – volume: vol. 13 start-page: 2531 year: 2020 end-page: 2537 ident: bib28 article-title: Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm publication-title: IET Power Electron – volume: vol. 51 year: 2022 ident: bib3 article-title: Parameter identification and state of charge estimation of Li-Ion batteries used in electric vehicles using artificial hummingbird optimizer publication-title: J. Energy Storage – volume: vol. 35 start-page: 470 year: 2021 end-page: 499 ident: bib25 article-title: Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives publication-title: Energy Storage Mater. – volume: vol. 21 start-page: 504 year: 2006 end-page: 511 ident: bib7 article-title: Accurate electrical battery model capable of predicting runtime and I-V performance publication-title: IEEE Trans. Energy Convers. – volume: vol. 9 start-page: 162059 year: 2021 end-page: 162080 ident: bib10 article-title: Northern Goshawk optimization: a new swarm-based algorithm for solving optimization problems publication-title: IEEE Access – volume: vol. 9 start-page: 2577 year: 2023 end-page: 2590 ident: bib18 article-title: HFCM-LSTM: a novel hybrid framework for state-of-health estimation of lithium-ion battery publication-title: Energy Rep. – volume: vol. 8 start-page: 774 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib21 article-title: Performance evaluation strategy for battery pack of electric vehicles: online estimation and offline evaluation publication-title: Energy Rep. doi: 10.1016/j.egyr.2022.02.026 – volume: vol. 307 start-page: 474 year: 2019 ident: 10.1016/j.egyr.2024.01.073_bib27 article-title: Electrochimica acta parameter identi fi cation and systematic validation of an enhanced single-particle model with aging degradation physics for Li-ion batteries publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2019.03.199 – volume: vol. 5 start-page: 4377 year: 2017 ident: 10.1016/j.egyr.2024.01.073_bib33 article-title: A sensitivity-based group-wise parameter identification algorithm for the electric model of Li-ion battery publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2678598 – volume: vol. 35 start-page: 10733 issue: 15 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib32 article-title: Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization publication-title: Neural Comput. Appl. doi: 10.1007/s00521-023-08261-1 – volume: vol. 3 start-page: 289 issue: 2 year: 2009 ident: 10.1016/j.egyr.2024.01.073_bib37 article-title: Experimental validation of a battery dynamic model for EV applications publication-title: World Electr. Veh. J. 2009 – volume: vol. 64 start-page: 367 year: 2017 ident: 10.1016/j.egyr.2024.01.073_bib30 article-title: A class of Monotone Fuzzy rule-based Wiener systems with an application to Li-ion battery modelling publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2017.06.029 – volume: vol. 131 start-page: 195 year: 2016 ident: 10.1016/j.egyr.2024.01.073_bib26 article-title: Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm–Nelder–Mead (PSO–NM) optimization algorithm publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2015.10.018 – volume: vol. 45 start-page: 12825 issue: 9 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib19 article-title: Parameter identification of fractional-order model with transfer learning for aging lithium-ion batteries publication-title: Int. J. Energy Res. doi: 10.1002/er.6614 – volume: vol. 13 start-page: 2531 issue: 12 year: 2020 ident: 10.1016/j.egyr.2024.01.073_bib28 article-title: Parameter identification of a lithium-ion battery based on the improved recursive least square algorithm publication-title: IET Power Electron doi: 10.1049/iet-pel.2019.1589 – volume: vol. 97 start-page: 448 year: 2020 ident: 10.1016/j.egyr.2024.01.073_bib43 article-title: A novel fractional variable-order equivalent circuit model and parameter identification of electric vehicle Li-ion batteries publication-title: ISA Trans. doi: 10.1016/j.isatra.2019.08.004 – volume: vol. 376 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib1 article-title: The arithmetic optimization algorithm publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2020.113609 – volume: vol. 60 start-page: 819 issue: 3 year: 2011 ident: 10.1016/j.egyr.2024.01.073_bib5 article-title: Novel predictive electric Li-ion battery model incorporating thermal and rate factor effects publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2010.2103333 – volume: vol. 21 start-page: 504 issue: 2 year: 2006 ident: 10.1016/j.egyr.2024.01.073_bib7 article-title: Accurate electrical battery model capable of predicting runtime and I-V performance publication-title: IEEE Trans. Energy Convers. doi: 10.1109/TEC.2006.874229 – volume: vol. 10 start-page: 1960 issue: 3 year: 2014 ident: 10.1016/j.egyr.2024.01.073_bib11 article-title: A supervisory power-splitting approach for a new ultracapacitor-battery vehicle deploying two propulsion machines publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2014.2299237 – volume: vol. 44 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib34 article-title: Parameter identification method for lithium-ion batteries based on recursive least square with sliding window difference forgetting factor publication-title: J. Energy Storage doi: 10.1016/j.est.2021.103485 – volume: vol. 7 start-page: 7170 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib31 article-title: Optimal parameter identification of linear and non-linear models for Li-Ion Battery Cells publication-title: Energy Rep. doi: 10.1016/j.egyr.2021.10.086 – volume: vol. 28 start-page: 1429 issue: 3 year: 2013 ident: 10.1016/j.egyr.2024.01.073_bib12 article-title: Comparison, selection, and parameterization of electrical battery models for automotive applications publication-title: IEEE Trans. Power Electron doi: 10.1109/TPEL.2012.2210564 – volume: vol. 8 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib9 article-title: Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems publication-title: Front. Mech. Eng. doi: 10.3389/fmech.2022.1126450 – volume: vol. 64 start-page: 367 year: 2017 ident: 10.1016/j.egyr.2024.01.073_bib29 article-title: Engineering applications of artificial intelligence A class of Monotone Fuzzy rule-based Wiener systems with an application to Li-ion battery modelling publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2017.06.029 – volume: vol. 10 start-page: 2710 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib13 article-title: Enhancing battery capacity estimation accuracy using the bald eagle search algorithm publication-title: Energy Rep. doi: 10.1016/j.egyr.2023.09.082 – volume: vol. 45 start-page: 16741 issue: 11 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib16 article-title: Optimal parameter identification strategy applied to lithium-ion battery model publication-title: Int. J. Energy Res. doi: 10.1002/er.6921 – volume: vol. 10 start-page: 49445 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib38 article-title: Zebra optimization algorithm: a new bio-inspired optimization algorithm for solving optimization algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3172789 – volume: vol. 114 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib44 article-title: Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.105075 – volume: vol. 448 year: 2020 ident: 10.1016/j.egyr.2024.01.073_bib45 article-title: Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2019.227575 – volume: vol. 7 start-page: 4348 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib14 article-title: Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services publication-title: Energy Rep. doi: 10.1016/j.egyr.2021.07.029 – volume: vol. 7 start-page: 5562 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib40 article-title: A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries publication-title: Energy Rep. doi: 10.1016/j.egyr.2021.08.182 – volume: vol. 46 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib20 article-title: Battery parameter identification strategy based on modified coot optimization algorithm publication-title: J. Energy Storage doi: 10.1016/j.est.2021.103848 – volume: vol. 46 start-page: 10564 issue: 8 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib15 article-title: Robust parameter estimation approach of Lithium-ion batteries employing bald eagle search algorithm publication-title: Int. J. Energy Res. doi: 10.1002/er.7834 – volume: vol. 134 start-page: 357 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib17 article-title: Modified bald eagle search algorithm for lithium-ion battery model parameters extraction publication-title: ISA Trans. doi: 10.1016/j.isatra.2022.08.025 – volume: vol. 9 start-page: 2577 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib18 article-title: HFCM-LSTM: a novel hybrid framework for state-of-health estimation of lithium-ion battery publication-title: Energy Rep. doi: 10.1016/j.egyr.2023.01.109 – volume: vol. 8 start-page: 148702 year: 2020 ident: 10.1016/j.egyr.2024.01.073_bib4 article-title: A comprehensive review of hybrid energy storage systems: converter topologies, control strategies and future prospects publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3015919 – volume: vol. 9 start-page: 1937 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib8 article-title: SOC estimation of retired lithium-ion batteries for electric vehicle with improved particle filter by H-infinity filter publication-title: Energy Rep. doi: 10.1016/j.egyr.2023.01.018 – volume: vol. 208 year: 2020 ident: 10.1016/j.egyr.2024.01.073_bib41 article-title: Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2020.112595 – volume: vol. 9 start-page: 162059 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib10 article-title: Northern Goshawk optimization: a new swarm-based algorithm for solving optimization problems publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3133286 – volume: vol. 45 start-page: 7326 issue: 5 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib24 article-title: Online internal short circuit detection method considering equalization electric quantity for lithium-ion battery pack in electric vehicles publication-title: Int. J. Energy Res. doi: 10.1002/er.6315 – volume: vol. 8 start-page: 13723 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib36 article-title: Lithium-ion battery charging optimization based on electrical, thermal and aging mechanism models publication-title: Energy Rep. doi: 10.1016/j.egyr.2022.10.059 – volume: vol. 26 start-page: 951 issue: 3 year: 2013 ident: 10.1016/j.egyr.2024.01.073_bib2 article-title: Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2012.09.013 – volume: vol. 35 start-page: 470 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib25 article-title: Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives publication-title: Energy Storage Mater. doi: 10.1016/j.ensm.2020.11.026 – volume: vol. 532 start-page: 183 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib35 article-title: RIME: a physics-based optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.02.010 – volume: vol. 289 start-page: 376 year: 2018 ident: 10.1016/j.egyr.2024.01.073_bib39 article-title: Electrochimica acta an online method to simultaneously identify the parameters and estimate states for lithium ion batteries publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2018.08.076 – volume: vol. 9 start-page: 1152 year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib42 article-title: Lithium-ion battery state of charge prediction based on machine learning approach publication-title: Energy Rep. doi: 10.1016/j.egyr.2023.03.091 – volume: vol. 51 year: 2022 ident: 10.1016/j.egyr.2024.01.073_bib3 article-title: Parameter identification and state of charge estimation of Li-Ion batteries used in electric vehicles using artificial hummingbird optimizer publication-title: J. Energy Storage – volume: vol. 7 start-page: 320 year: 2021 ident: 10.1016/j.egyr.2024.01.073_bib6 article-title: A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects publication-title: Energy Rep. doi: 10.1016/j.egyr.2021.06.051 – volume: vol. 165 start-page: 153 year: 2018 ident: 10.1016/j.egyr.2024.01.073_bib22 article-title: Lithium-ion battery modeling and parameter identification based on fractional theory publication-title: Energy doi: 10.1016/j.energy.2018.09.101 – year: 2023 ident: 10.1016/j.egyr.2024.01.073_bib23 article-title: An improved design of knee orthosis using self ‑ adaptive bonobo optimizer ( SaBO) publication-title: J. Intell. Robot. Syst. |
| SSID | ssj0001920463 |
| Score | 2.3867383 |
| Snippet | The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric... |
| SourceID | hal crossref elsevier |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 2049 |
| SubjectTerms | Electric vehicles Engineering Sciences Li-ion battery Metaheuristic optimization algorithms Parameters identification |
| Title | Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data |
| URI | https://dx.doi.org/10.1016/j.egyr.2024.01.073 https://nantes-universite.hal.science/hal-05082174 |
| Volume | 11 |
| WOSCitedRecordID | wos001174787400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2352-4847 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920463 issn: 2352-4847 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2352-4847 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001920463 issn: 2352-4847 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLa6wYELAgFi_JgsxK3KlB9OYh-naVMP00Aw0G6RYzu025pObVqtm8Qfxl_Hs5-TdoNN7MDFqtzGSfO-2O-9fO8zIR-jONIyrXTABIemLLNAGCbhiWdRrkwiMyfH8P0wPzriJyfic6_3q62FWZzndc0vL8XFfzU19IGxbensA8zdDQod8BmMDi2YHdp_MvwnmATGtr5KWt6V1UAcaU8JQmPPUJB22ZfeAwX3E5zx4Wg-Duz3pZPcXOImOSgJ7vbKGan-wgwdja4_dykGPbW8I7VUtvoKi9xWaX4sKvTvJDqaz1Dq0tNrB5P5bCbH_X2t117ufzFXZ7ge_pBXXb7_wMCk2CCx96tZaogeVjnY6ZlsMLvt6fk-ixGzFdsKJ7sYHMGAcVTf3DF_6Wtn62h9ug1R79Qv3XGIMvB_LAuYoTjdgXtrNWBj5qRacQ-Vmxrct9bGjrHYkuFOCztGYccowqiAMTbIoziHsMzSRn-u5fdEbMXY3N6G_m_4mi2kF96-lLv8oo1hm-F3Hs_xM_LUhyp0FyH2nPRM_YJce3jRDl70JrxoCy_q4UWbCV2DF_Xwog5eFOBFW3jRFl7UwYs6eFEHL2rh9ZJ8O9g_3hsEfgOPQCU5b4JMJDrNhVFMixLiiCqHeJ1HhsvEqiJJw8sqifOS6yo3rIQ5Iwwl01WsZKpSkySvyGY9qc1rQjMuKqMTboSoGCs1N5mKIDJMIL7XjKktErW3r1Be3d5usnJe3G25LdLvjrlAbZd7f522Vim8d4peZwEou_e4D2DC7gRWzn2we1jYvhCiI5sSWERvHnQpb8mT1TP0jmw207l5Tx6rRTOaTbddWmnbofE36ZnCwA |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimal+parameter+identification+strategy+applied+to+lithium-ion+battery+model+for+electric+vehicles+using+drive+cycle+data&rft.jtitle=Energy+reports&rft.au=Ghadbane%2C+Houssam+Eddine&rft.au=Rezk%2C+Hegazy&rft.au=Ferahtia%2C+Seydali&rft.au=Barkat%2C+Said&rft.date=2024-06-01&rft.issn=2352-4847&rft.eissn=2352-4847&rft.volume=11&rft.spage=2049&rft.epage=2058&rft_id=info:doi/10.1016%2Fj.egyr.2024.01.073&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_egyr_2024_01_073 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2352-4847&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2352-4847&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2352-4847&client=summon |