An entropy-based, self-adaptive predictive algorithm for battery degradation
In order to avoid excess waste generation and provide much needed energy storage capacity, lithium ion (Li-ion) batteries, when retired from their 1st-life, can be repurposed or given a 2nd-life in lower-stress storage roles. To do so, and to determine for what purpose, accurately predicting the deg...
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| Veröffentlicht in: | Journal of power sources Jg. 656; S. 237920 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.11.2025
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| ISSN: | 0378-7753 |
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| Abstract | In order to avoid excess waste generation and provide much needed energy storage capacity, lithium ion (Li-ion) batteries, when retired from their 1st-life, can be repurposed or given a 2nd-life in lower-stress storage roles. To do so, and to determine for what purpose, accurately predicting the degradation rate of 2nd-life Li-ion batteries’ state of health is highly important, yet difficult, owing to the lack of available data from cells of sufficient aging variety. Additionally, as there are no formal standards on what information may come with potential 2nd-life batteries, it is hard to predict their subsequent behavior. While certain models do exist for predicting degradation of certain cell types/chemistries, such models typically rely on extensive data from the battery’s 1st-life and do not generalize well over different types of cell. This work aims to establish a novel entropy-based theoretical approach, and a novel entropy-based algorithm, for predicting 2nd-life batteries’ behavior. The proposed model hybridizes simple machine learning methods with a light weight model based on physics, centered around approximating the amounts of generated irreversible thermodynamic entropy and Shannon entropy. Tests of this model on three different Li-ion battery types (LFP, LCO, NMC) show that the model is able to make accurate predictions on 2nd-life battery lifetime while only requiring data from one single cycle. Subsequent sampling is shown to further improve model accuracy, placing this novel algorithm on par with state of the art ML-estimates, but without the need for extensive training or reliance on extensive data from 1st-life.
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•Method proposed uses a hybrid model that uses physics and information based entropy.•This approach is tested with 3 different ML types ranging from regression to a NN.•Method obtains results on par with the current state of the art in BMS SOH prediction.•Method only needs limited data, and is very quick to train, as it is lightweight.•This work explores self-adaptive modeling for 2nd-life batteries. |
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| AbstractList | In order to avoid excess waste generation and provide much needed energy storage capacity, lithium ion (Li-ion) batteries, when retired from their 1st-life, can be repurposed or given a 2nd-life in lower-stress storage roles. To do so, and to determine for what purpose, accurately predicting the degradation rate of 2nd-life Li-ion batteries’ state of health is highly important, yet difficult, owing to the lack of available data from cells of sufficient aging variety. Additionally, as there are no formal standards on what information may come with potential 2nd-life batteries, it is hard to predict their subsequent behavior. While certain models do exist for predicting degradation of certain cell types/chemistries, such models typically rely on extensive data from the battery's 1st-life and do not generalize well over different types of cell. This work aims to establish a novel entropy-based theoretical approach, and a novel entropy-based algorithm, for predicting 2nd-life batteries’ behavior. The proposed model hybridizes simple machine learning methods with a light weight model based on physics, centered around approximating the amounts of generated irreversible thermodynamic entropy and Shannon entropy. Tests of this model on three different Li-ion battery types (LFP, LCO, NMC) show that the model is able to make accurate predictions on 2nd-life battery lifetime while only requiring data from one single cycle. Subsequent sampling is shown to further improve model accuracy, placing this novel algorithm on par with state of the art ML-estimates, but without the need for extensive training or reliance on extensive data from 1st-life. In order to avoid excess waste generation and provide much needed energy storage capacity, lithium ion (Li-ion) batteries, when retired from their 1st-life, can be repurposed or given a 2nd-life in lower-stress storage roles. To do so, and to determine for what purpose, accurately predicting the degradation rate of 2nd-life Li-ion batteries’ state of health is highly important, yet difficult, owing to the lack of available data from cells of sufficient aging variety. Additionally, as there are no formal standards on what information may come with potential 2nd-life batteries, it is hard to predict their subsequent behavior. While certain models do exist for predicting degradation of certain cell types/chemistries, such models typically rely on extensive data from the battery’s 1st-life and do not generalize well over different types of cell. This work aims to establish a novel entropy-based theoretical approach, and a novel entropy-based algorithm, for predicting 2nd-life batteries’ behavior. The proposed model hybridizes simple machine learning methods with a light weight model based on physics, centered around approximating the amounts of generated irreversible thermodynamic entropy and Shannon entropy. Tests of this model on three different Li-ion battery types (LFP, LCO, NMC) show that the model is able to make accurate predictions on 2nd-life battery lifetime while only requiring data from one single cycle. Subsequent sampling is shown to further improve model accuracy, placing this novel algorithm on par with state of the art ML-estimates, but without the need for extensive training or reliance on extensive data from 1st-life. [Display omitted] •Method proposed uses a hybrid model that uses physics and information based entropy.•This approach is tested with 3 different ML types ranging from regression to a NN.•Method obtains results on par with the current state of the art in BMS SOH prediction.•Method only needs limited data, and is very quick to train, as it is lightweight.•This work explores self-adaptive modeling for 2nd-life batteries. |
| ArticleNumber | 237920 |
| Author | Evdokimova, Eva Wik, Torsten Strugnell-Lees, Benedick |
| Author_xml | – sequence: 1 givenname: Benedick orcidid: 0009-0007-0615-0236 surname: Strugnell-Lees fullname: Strugnell-Lees, Benedick email: benidick@chalmers.se organization: Department of Electrical Engineering, Chalmers University of Technology, Chalmersgatan 4, 412 96 Gothenburg, Sweden – sequence: 2 givenname: Eva orcidid: 0009-0005-4649-7176 surname: Evdokimova fullname: Evdokimova, Eva organization: Department of Bioscience, University of Skövde, Högskolevägen 1, 541 28 Skövde, Sweden – sequence: 3 givenname: Torsten orcidid: 0000-0002-5234-8426 surname: Wik fullname: Wik, Torsten organization: Department of Electrical Engineering, Chalmers University of Technology, Chalmersgatan 4, 412 96 Gothenburg, Sweden |
| BackLink | https://research.chalmers.se/publication/547874$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| Cites_doi | 10.3390/inventions4020023 10.1016/j.rser.2015.11.042 10.1016/j.jpowsour.2012.07.016 10.1109/TR.2014.2299152 10.1016/j.joule.2021.06.005 10.1016/j.eswa.2023.123123 10.1109/TMECH.2020.2975343 10.3390/batteries10050161 10.1016/j.est.2023.109741 10.1016/j.est.2021.103119 10.1016/j.jpowsour.2006.11.071 10.1016/j.rser.2021.111903 10.1016/j.ymssp.2024.111120 10.3390/batteries10030076 10.1016/j.ymssp.2022.109002 10.1016/j.est.2024.113604 10.1016/j.jpowsour.2018.08.064 10.1109/ACCESS.2021.3111927 10.1016/j.electacta.2020.137337 10.3390/batteries10100356 10.1016/j.jmsy.2021.03.019 10.1109/ACCESS.2024.3455255 10.3390/batteries9120571 10.1016/j.jechem.2020.10.017 10.1016/j.jpowsour.2018.10.019 10.1021/acsenergylett.4c01358 10.1016/j.egypro.2017.03.676 10.1016/j.jpowsour.2016.12.011 10.1016/j.rser.2019.109254 10.1109/TTE.2021.3138357 10.1038/s41560-019-0356-8 10.1109/TVT.2018.2865664 10.1016/j.joule.2019.11.018 10.1016/j.scitotenv.2004.04.070 |
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| Keywords | Thermodynamics Second-life Entropy Batteries State of health (SOH) |
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| References | Dubarry, Truchot, Liaw (b30) 2012; 219 Wang, Wang, Heringa, Bai, Wagemaker (b47) 2024 Jia, Ma, Guo, Li (b39) 2021; 61 Kainat, Anwer, Hamid, Gull, Khan (b49) 2023; 313 Plett (b21) 2016 Hu, Che, Lin, Deng (b26) 2020; 25 McKinsey & Company (b2) 2023 Sulzer, Mohtat, Aitio, Lee, Yeh, Steinbacher, Khan, Lee, Siegel, Stefanopoulou, Howey (b20) 2021; 5 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (b58) 2011; 12 International Electrotechnical Comission, Interlek (b11) 2022 Bole, Kulkarni, Daigle (b63) 2014; 6 Yao, Xu, Tang, Zhou, Hou, Xiao, Fu (b14) 2021; 12 Namdari, Li (b53) 2020; vol. 63 4 BloombergNEF (b1) 2023 Gray (b55) 2011 Osara, Ezekoye, Marr, Bryant (b52) 2021; 42 André (b64) 2004; 334–335 Bennett (b54) 2003; 34 Maddipatla, Kong, Pecht (b46) 2024; 10 The European Union, LEX (b5) 2006 Hu, Yuan, Zou, Li, Zhang (b16) 2018; 67 Xiong, Li, Tian (b23) 2018; 405 Kohtz, Xu, Zheng, Wang (b33) 2022; 172 International Energy Agency (b8) 2024 Brillouin (b43) 1962 Severson, Attia, Jin (b60) 2019; 4 Börner, Frieges, Späth, Spütz, Heimes, Sauer, Li (b25) 2022; 3 S&P Global Commodity Insights (b3) 2024 Plett (b24) 2015 Rauf, Khalid, Arshad (b37) 2022; 156 Abo Gamra, Zähringer, Ladner, Allgäuer, Lienkamp (b66) 2024; 15 World Energy Outlook Special Report (b4) 2024 Hassan, Khan, Li, Su, Zhou, Wang, Wang (b6) 2023; 9 Osara, Bryant (b44) 2019; 4 International Electrotechnical Comission (b12) 2022 Esperilla, Félez, Romero, Carretero (b50) 2007; 165 Azizighalehsari, Singh, Soeiro, Rietveld (b9) 2024; 10 Birkl (b10) 2016; 341 Lucu, Martinez-Laserna, Gandiaga, Camblong (b22) 2018; 401 Bak, Lee (b38) 2019; 11 Chen, Kang, Zhao, Wang, Liu, Li, Liang, He, Li, Tavajohi, Li (b48) 2021; 59 Severson, Attia, Jin (b65) 2017 Vignesh, Che, Selvaraj, Tey, Lee, Shareef, Errouissi (b29) 2024; 369 Verleysen, Rossi, François (b57) 2009 Noura, Boulon, Jemei (b28) 2020; 44 Thi Minh Lien, Quoc Anh, Duc Tuyen, Fujita (b32) 2024; 12 Sorouri, Oshnoei, Che, Teodorescu (b36) 2024; 100 Vermeer, Chandra Mouli, Bauer (b19) 2022; 8 Alsuwian, Ansari, Ammirrul Atiqi Mohd Zainuri, Ayob, Hussain, Hossain Lipu, Alhawari, Almawgani, Almasabi, Taher Hindi (b35) 2024; 246 Chen (b62) 2024 Gu, Bai, Cui, Zhu, Zhuang, Li, Hu, Song (b7) 2023; 192 Dineva (b45) 2024; 10 Li, Zhang, Li, Si (b34) 2024; 209 United Nations Economic Commission for Europe, section 38.3 (b13) 2023 Sun, Liu, Wang (b56) 2017; 105 Oji, Zhou, Ci, Kang, Chen, X. (b31) 2021; 9 Osara, Bryant (b41) 2021; 365 Hu, Xu, Lin, Pecht (b17) 2020; 4 Yang, Yang, Meng, Song, He, Cai, Xie, Xu (b40) 2024; 75 Li, Liu, Foley, Zulke, Berecibar, Nanini-Maury, Mierlo, Hoster (b18) 2019; 113 Howey, Birk (b67) 2017 Birkl (b61) 2017 Berecibar, Gandiaga, Villarreal, Omar, Van Mierlo, Van den Bossche (b15) 2016; 56 Kondepudi, Prigogine (b51) 1998 Liao, Köttig (b27) 2014; 63 Paszke, Gross, Chintala, Chanan, Yang, DeVito, Lin, Desmaison, Antiga, Lerer (b59) 2017 Atkins, Jones (b42) 2010 Dubarry (10.1016/j.jpowsour.2025.237920_b30) 2012; 219 Osara (10.1016/j.jpowsour.2025.237920_b41) 2021; 365 Chen (10.1016/j.jpowsour.2025.237920_b62) 2024 Gray (10.1016/j.jpowsour.2025.237920_b55) 2011 Liao (10.1016/j.jpowsour.2025.237920_b27) 2014; 63 André (10.1016/j.jpowsour.2025.237920_b64) 2004; 334–335 Howey (10.1016/j.jpowsour.2025.237920_b67) 2017 Paszke (10.1016/j.jpowsour.2025.237920_b59) 2017 S&P Global Commodity Insights (10.1016/j.jpowsour.2025.237920_b3) 2024 International Electrotechnical Comission, Interlek (10.1016/j.jpowsour.2025.237920_b11) 2022 Hu (10.1016/j.jpowsour.2025.237920_b17) 2020; 4 Kohtz (10.1016/j.jpowsour.2025.237920_b33) 2022; 172 Jia (10.1016/j.jpowsour.2025.237920_b39) 2021; 61 Severson (10.1016/j.jpowsour.2025.237920_b60) 2019; 4 Hassan (10.1016/j.jpowsour.2025.237920_b6) 2023; 9 Yang (10.1016/j.jpowsour.2025.237920_b40) 2024; 75 Maddipatla (10.1016/j.jpowsour.2025.237920_b46) 2024; 10 Vermeer (10.1016/j.jpowsour.2025.237920_b19) 2022; 8 BloombergNEF (10.1016/j.jpowsour.2025.237920_b1) 2023 Kainat (10.1016/j.jpowsour.2025.237920_b49) 2023; 313 Li (10.1016/j.jpowsour.2025.237920_b18) 2019; 113 Osara (10.1016/j.jpowsour.2025.237920_b44) 2019; 4 Namdari (10.1016/j.jpowsour.2025.237920_b53) 2020; vol. 63 4 Azizighalehsari (10.1016/j.jpowsour.2025.237920_b9) 2024; 10 Verleysen (10.1016/j.jpowsour.2025.237920_b57) 2009 Plett (10.1016/j.jpowsour.2025.237920_b24) 2015 Esperilla (10.1016/j.jpowsour.2025.237920_b50) 2007; 165 Birkl (10.1016/j.jpowsour.2025.237920_b61) 2017 United Nations Economic Commission for Europe, section 38.3 (10.1016/j.jpowsour.2025.237920_b13) 2023 Plett (10.1016/j.jpowsour.2025.237920_b21) 2016 Hu (10.1016/j.jpowsour.2025.237920_b26) 2020; 25 Abo Gamra (10.1016/j.jpowsour.2025.237920_b66) 2024; 15 Bak (10.1016/j.jpowsour.2025.237920_b38) 2019; 11 McKinsey & Company (10.1016/j.jpowsour.2025.237920_b2) 2023 Bennett (10.1016/j.jpowsour.2025.237920_b54) 2003; 34 Oji (10.1016/j.jpowsour.2025.237920_b31) 2021; 9 Berecibar (10.1016/j.jpowsour.2025.237920_b15) 2016; 56 Li (10.1016/j.jpowsour.2025.237920_b34) 2024; 209 Xiong (10.1016/j.jpowsour.2025.237920_b23) 2018; 405 Thi Minh Lien (10.1016/j.jpowsour.2025.237920_b32) 2024; 12 Yao (10.1016/j.jpowsour.2025.237920_b14) 2021; 12 Rauf (10.1016/j.jpowsour.2025.237920_b37) 2022; 156 Wang (10.1016/j.jpowsour.2025.237920_b47) 2024 Severson (10.1016/j.jpowsour.2025.237920_b65) 2017 Brillouin (10.1016/j.jpowsour.2025.237920_b43) 1962 Kondepudi (10.1016/j.jpowsour.2025.237920_b51) 1998 Gu (10.1016/j.jpowsour.2025.237920_b7) 2023; 192 International Electrotechnical Comission (10.1016/j.jpowsour.2025.237920_b12) 2022 Chen (10.1016/j.jpowsour.2025.237920_b48) 2021; 59 Noura (10.1016/j.jpowsour.2025.237920_b28) 2020; 44 Bole (10.1016/j.jpowsour.2025.237920_b63) 2014; 6 Sulzer (10.1016/j.jpowsour.2025.237920_b20) 2021; 5 Hu (10.1016/j.jpowsour.2025.237920_b16) 2018; 67 Dineva (10.1016/j.jpowsour.2025.237920_b45) 2024; 10 Birkl (10.1016/j.jpowsour.2025.237920_b10) 2016; 341 Vignesh (10.1016/j.jpowsour.2025.237920_b29) 2024; 369 Pedregosa (10.1016/j.jpowsour.2025.237920_b58) 2011; 12 Börner (10.1016/j.jpowsour.2025.237920_b25) 2022; 3 Lucu (10.1016/j.jpowsour.2025.237920_b22) 2018; 401 World Energy Outlook Special Report (10.1016/j.jpowsour.2025.237920_b4) 2024 Osara (10.1016/j.jpowsour.2025.237920_b52) 2021; 42 Sun (10.1016/j.jpowsour.2025.237920_b56) 2017; 105 The European Union, LEX (10.1016/j.jpowsour.2025.237920_b5) 2006 International Energy Agency (10.1016/j.jpowsour.2025.237920_b8) 2024 Atkins (10.1016/j.jpowsour.2025.237920_b42) 2010 Alsuwian (10.1016/j.jpowsour.2025.237920_b35) 2024; 246 Sorouri (10.1016/j.jpowsour.2025.237920_b36) 2024; 100 |
| References_xml | – volume: 219 start-page: 204 year: 2012 end-page: 216 ident: b30 article-title: Synthesize battery degradation modes via a diagnostic and prognostic model publication-title: J. Power Sources – volume: 369 start-page: 5 year: 2024 end-page: 45 ident: b29 article-title: State of health (SoH) estimation methods for second life lithium-ion battery—Review and challenges publication-title: Appl. Energy – year: 2022 ident: b11 article-title: IEC 62133: Safety testing for lithium ion batteries – year: 2022 ident: b12 article-title: IEC 62619:2022 – year: 2023 ident: b1 article-title: New energy outlook series — bloombergnef — bloomberg finance LP – volume: 12 start-page: 129022 year: 2024 end-page: 129039 ident: b32 article-title: Prediction of state-of-health and remaining-useful-life of battery based on hybrid neural network model publication-title: IEEE Access – volume: 15 year: 2024 ident: b66 article-title: Examining model-based fast-charging and preconditioning on a vehicle level publication-title: World Electr. Veh. J. – volume: 209 year: 2024 ident: b34 article-title: A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities publication-title: Mech. Syst. Signal Process. – volume: 10 year: 2024 ident: b46 article-title: Safety analysis of lithium-ion cylindrical batteries using design and process failure mode and effect analysis publication-title: Batteries – year: 2006 ident: b5 article-title: Directive 2006/66/EC of the European parliament and of the council – year: 1962 ident: b43 article-title: Science and Information Theory – volume: 156 year: 2022 ident: b37 article-title: Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling publication-title: Renew. Sustain. Energy Rev. – year: 2024 ident: b4 article-title: Batteries and secure energy transitions – volume: 11 year: 2019 ident: b38 article-title: Accurate estimation of battery SOH and RUL based on a progressive LSTM with a time compensated entropy index publication-title: Annu. Conf. PHM Soc. – year: 2023 ident: b13 article-title: Manual of tests and criteria, revision 8 – volume: 172 start-page: 2 year: 2022 end-page: 15 ident: b33 article-title: Physics-informed machine learning model for battery state of health prognostics using partial charging segments publication-title: Mech. Syst. Signal Process. – volume: 405 start-page: 18 year: 2018 end-page: 29 ident: b23 article-title: Towards a smarter battery management system: A critical review on battery state of health monitoring methods publication-title: J. Power Sources – volume: 165 start-page: 436 year: 2007 end-page: 445 ident: b50 article-title: A full model for simulation of electrochemical cells including complex behavior publication-title: J. Power Sources – year: 2017 ident: b65 article-title: Data-driven prediction of battery cycle life before capacity degradation, batch 2017-06-30 – volume: 401 start-page: 85 year: 2018 end-page: 101 ident: b22 article-title: A critical review on self-adaptive Li-ion battery ageing models publication-title: J. Power Sources – year: 2011 ident: b55 article-title: Entropy and Information Theory – year: 2024 ident: b47 article-title: High-entropy electrolytes for lithium-ion batteries publication-title: ACS Energy Lett. – volume: 100 year: 2024 ident: b36 article-title: A comprehensive review of hybrid battery state of charge estimation: Exploring physics-aware AI-based approaches publication-title: J. Energy Storage – volume: 3 year: 2022 ident: b25 article-title: Challenges of second-life concepts for retired electric vehicle batteries publication-title: Cell Rep. Phys. Sci. – year: 1998 ident: b51 article-title: Modern Thermodynamics: From Heat Engines to Dissipative Structures – volume: 10 year: 2024 ident: b45 article-title: Evaluation of advances in battery health prediction for electric vehicles from traditional linear filters to latest machine learning approaches publication-title: Batteries – year: 2024 ident: b8 article-title: Trends in electric vehicle batteries - Global EV outlook 2024 - analysis - IEA – year: 2024 ident: b3 article-title: New global battery energy storage systems capacity doubles in 2023, IEA says – volume: 25 start-page: 1 year: 2020 end-page: 10 ident: b26 article-title: Health prognosis for electric vehicle battery packs: A data-driven approach publication-title: IEEE/ASME Trans. Mechatronics – year: 2017 ident: b59 article-title: Automatic differentiation in pytorch publication-title: OpenReview – volume: 75 year: 2024 ident: b40 article-title: Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model publication-title: J. Energy Storage – volume: 246 year: 2024 ident: b35 article-title: A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application publication-title: Expert Syst. Appl. – volume: 56 start-page: 572 year: 2016 end-page: 587 ident: b15 article-title: Critical review of state of health estimation methods of Li-ion batteries for real applications publication-title: Renew. Sustain. Energy Rev. – volume: 4 start-page: 383 year: 2019 end-page: 391 ident: b60 article-title: Data-driven prediction of battery cycle life before capacity degradation. publication-title: Nat. Energy – volume: 42 year: 2021 ident: b52 article-title: A methodology for analyzing aging and performance of lithium-ion batteries: Consistent cycling application publication-title: J. Energy Storage – volume: 192 start-page: 1 year: 2023 end-page: 20 ident: b7 article-title: Challenges and opportunities for second-life batteries: Key technologies and economy publication-title: Renew. Sustain. Energy Rev. – volume: 4 start-page: 310 year: 2020 end-page: 346 ident: b17 article-title: Battery lifetime prognostics publication-title: Joule – year: 2017 ident: b61 article-title: Oxford battery degradation dataset 1 publication-title: Univ. Oxf. – volume: 10 start-page: 1 year: 2024 end-page: 39 ident: b9 article-title: Empowering electric vehicles batteries: A comprehensive look at the application and challenges of second-life batteries publication-title: Batteries – year: 2010 ident: b42 article-title: Chemical Principles: The Quest for Insight – year: 2024 ident: b62 article-title: NASA lithium ion battery dataset publication-title: IEEE Dataport – volume: 59 start-page: 83 year: 2021 end-page: 99 ident: b48 article-title: A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards publication-title: J. Energy Chem. – volume: 67 start-page: 319 year: 2018 end-page: 10 329 ident: b16 article-title: Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus publication-title: IEEE Trans. Veh. Technol. – volume: 4 year: 2019 ident: b44 article-title: A thermodynamic model for lithium-ion battery degradation: Application of the degradation-entropy generation theorem publication-title: Inventions – volume: 113 year: 2019 ident: b18 article-title: Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review publication-title: Renew. Sustain. Energy Rev. – volume: 61 start-page: 773 year: 2021 end-page: 781 ident: b39 article-title: A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine publication-title: J. Manuf. Syst. – volume: 9 start-page: 126903 year: 2021 end-page: 126916 ident: b31 article-title: Data-driven methods for battery SOH estimation: Survey and a critical analysis publication-title: IEEE Access – volume: 334–335 start-page: 73 year: 2004 end-page: 84 ident: b64 article-title: The ARTEMIS European driving cycles for measuring car pollutant emissions publication-title: Sci. Total Environ. – year: 2017 ident: b67 article-title: Oxford battery degradation dataset 1 publication-title: Univ. Oxf. – volume: 12 start-page: 1 year: 2021 end-page: 18 ident: b14 article-title: A review of lithium-ion battery state of health estimation and prediction methods publication-title: World Electr. Veh. – volume: 5 start-page: 1934 year: 2021 end-page: 1955 ident: b20 article-title: The challenge and opportunity of battery lifetime prediction from field data publication-title: Joule – volume: 34 start-page: 1 year: 2003 end-page: 10 ident: b54 article-title: Notes on Landauer’s principle, reversible computation, and Maxwell’s Demon publication-title: Elsevier Sci. – start-page: 52 year: 2009 end-page: 69 ident: b57 article-title: Advances in feature selection with mutual information publication-title: Similarity-Based Clustering: Recent Developments and Biomedical Applications – volume: vol. 63 4 start-page: 1 year: 2020 end-page: 7 ident: b53 article-title: An entropy-based approach for modeling lithium-ion battery capacity fade publication-title: 2020 Annual Reliability and Maintainability Symposium – volume: 9 start-page: 3 year: 2023 end-page: 21 ident: b6 article-title: Second-life batteries: A review on power grid applications, degradation mechanisms, and power electronics interface architectures publication-title: Batteries – volume: 63 start-page: 191 year: 2014 end-page: 207 ident: b27 article-title: Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction publication-title: IEEE Trans. Reliab. – volume: 365 start-page: 1 year: 2021 end-page: 14 ident: b41 article-title: Performance and degradation characterization of electrochemical power sources using thermodynamics publication-title: Electrochim. Acta – year: 2023 ident: b2 article-title: The trends shaping the energy transition – volume: 341 start-page: 373 year: 2016 end-page: 386 ident: b10 article-title: Degradation diagnostics for lithium ion cells publication-title: J. Power Sources – year: 2015 ident: b24 article-title: Battery Management Systems Volume 1, Battery Modeling – volume: 44 start-page: 4 year: 2020 end-page: 16 ident: b28 article-title: A review of battery state of health estimation methods: Hybrid electric vehicle challenges publication-title: World Electr. Veh. J. – volume: 12 year: 2011 ident: b58 article-title: Scikit-learn: Machine learning in python publication-title: J. Mach. Learn. Res. – volume: 8 start-page: 2205 year: 2022 end-page: 2232 ident: b19 article-title: A comprehensive review on the characteristics and modeling of lithium-ion battery aging publication-title: IEEE Trans. Transp. Electrification – volume: 313 year: 2023 ident: b49 article-title: Electrolytes in lithium-ion batteries: Advancements in the era of twenties (2020’s) publication-title: Mater. Chem. Phys. – volume: 105 start-page: 2354 year: 2017 end-page: 2359 ident: b56 article-title: Real-time fault diagnosis method of battery system based on Shannon entropy publication-title: Energy Procedia – year: 2016 ident: b21 article-title: Battery Management Systems Volume 2, Equivalent-Circuit Methods – volume: 6 year: 2014 ident: b63 article-title: Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use publication-title: Annu. Conf. PHM Soc. – year: 2015 ident: 10.1016/j.jpowsour.2025.237920_b24 – volume: 4 issue: 2 year: 2019 ident: 10.1016/j.jpowsour.2025.237920_b44 article-title: A thermodynamic model for lithium-ion battery degradation: Application of the degradation-entropy generation theorem publication-title: Inventions doi: 10.3390/inventions4020023 – volume: 56 start-page: 572 year: 2016 ident: 10.1016/j.jpowsour.2025.237920_b15 article-title: Critical review of state of health estimation methods of Li-ion batteries for real applications publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.11.042 – volume: 219 start-page: 204 year: 2012 ident: 10.1016/j.jpowsour.2025.237920_b30 article-title: Synthesize battery degradation modes via a diagnostic and prognostic model publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2012.07.016 – year: 2011 ident: 10.1016/j.jpowsour.2025.237920_b55 – volume: 63 start-page: 191 year: 2014 ident: 10.1016/j.jpowsour.2025.237920_b27 article-title: Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2014.2299152 – year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b62 article-title: NASA lithium ion battery dataset publication-title: IEEE Dataport – volume: 6 issue: 1 year: 2014 ident: 10.1016/j.jpowsour.2025.237920_b63 article-title: Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use publication-title: Annu. Conf. PHM Soc. – year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b4 – volume: 5 start-page: 1934 issue: 8 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b20 article-title: The challenge and opportunity of battery lifetime prediction from field data publication-title: Joule doi: 10.1016/j.joule.2021.06.005 – volume: 246 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b35 article-title: A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2023.123123 – volume: 44 start-page: 4 year: 2020 ident: 10.1016/j.jpowsour.2025.237920_b28 article-title: A review of battery state of health estimation methods: Hybrid electric vehicle challenges publication-title: World Electr. Veh. J. – year: 1998 ident: 10.1016/j.jpowsour.2025.237920_b51 – year: 2006 ident: 10.1016/j.jpowsour.2025.237920_b5 – volume: 25 start-page: 1 year: 2020 ident: 10.1016/j.jpowsour.2025.237920_b26 article-title: Health prognosis for electric vehicle battery packs: A data-driven approach publication-title: IEEE/ASME Trans. Mechatronics doi: 10.1109/TMECH.2020.2975343 – year: 1962 ident: 10.1016/j.jpowsour.2025.237920_b43 – year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b2 – volume: 313 year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b49 article-title: Electrolytes in lithium-ion batteries: Advancements in the era of twenties (2020’s) publication-title: Mater. Chem. Phys. – year: 2017 ident: 10.1016/j.jpowsour.2025.237920_b59 article-title: Automatic differentiation in pytorch publication-title: OpenReview – volume: 10 start-page: 1 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b9 article-title: Empowering electric vehicles batteries: A comprehensive look at the application and challenges of second-life batteries publication-title: Batteries doi: 10.3390/batteries10050161 – volume: 12 year: 2011 ident: 10.1016/j.jpowsour.2025.237920_b58 article-title: Scikit-learn: Machine learning in python publication-title: J. Mach. Learn. Res. – volume: vol. 63 4 start-page: 1 year: 2020 ident: 10.1016/j.jpowsour.2025.237920_b53 article-title: An entropy-based approach for modeling lithium-ion battery capacity fade – volume: 3 issue: 10 year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b25 article-title: Challenges of second-life concepts for retired electric vehicle batteries publication-title: Cell Rep. Phys. Sci. – volume: 75 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b40 article-title: Joint evaluation and prediction of SOH and RUL for lithium batteries based on a GBLS booster multi-task model publication-title: J. Energy Storage doi: 10.1016/j.est.2023.109741 – volume: 42 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b52 article-title: A methodology for analyzing aging and performance of lithium-ion batteries: Consistent cycling application publication-title: J. Energy Storage doi: 10.1016/j.est.2021.103119 – volume: 165 start-page: 436 issue: 1 year: 2007 ident: 10.1016/j.jpowsour.2025.237920_b50 article-title: A full model for simulation of electrochemical cells including complex behavior publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2006.11.071 – volume: 156 year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b37 article-title: Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2021.111903 – year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b12 – volume: 209 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b34 article-title: A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2024.111120 – volume: 192 start-page: 1 year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b7 article-title: Challenges and opportunities for second-life batteries: Key technologies and economy publication-title: Renew. Sustain. Energy Rev. – volume: 10 issue: 3 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b46 article-title: Safety analysis of lithium-ion cylindrical batteries using design and process failure mode and effect analysis publication-title: Batteries doi: 10.3390/batteries10030076 – volume: 172 start-page: 2 year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b33 article-title: Physics-informed machine learning model for battery state of health prognostics using partial charging segments publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2022.109002 – year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b3 – volume: 100 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b36 article-title: A comprehensive review of hybrid battery state of charge estimation: Exploring physics-aware AI-based approaches publication-title: J. Energy Storage doi: 10.1016/j.est.2024.113604 – volume: 401 start-page: 85 year: 2018 ident: 10.1016/j.jpowsour.2025.237920_b22 article-title: A critical review on self-adaptive Li-ion battery ageing models publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.08.064 – volume: 9 start-page: 126903 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b31 article-title: Data-driven methods for battery SOH estimation: Survey and a critical analysis publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3111927 – year: 2017 ident: 10.1016/j.jpowsour.2025.237920_b65 – volume: 12 start-page: 1 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b14 article-title: A review of lithium-ion battery state of health estimation and prediction methods publication-title: World Electr. Veh. – volume: 365 start-page: 1 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b41 article-title: Performance and degradation characterization of electrochemical power sources using thermodynamics publication-title: Electrochim. Acta doi: 10.1016/j.electacta.2020.137337 – volume: 10 issue: 10 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b45 article-title: Evaluation of advances in battery health prediction for electric vehicles from traditional linear filters to latest machine learning approaches publication-title: Batteries doi: 10.3390/batteries10100356 – volume: 15 issue: 8 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b66 article-title: Examining model-based fast-charging and preconditioning on a vehicle level publication-title: World Electr. Veh. J. – volume: 61 start-page: 773 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b39 article-title: A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine publication-title: J. Manuf. Syst. doi: 10.1016/j.jmsy.2021.03.019 – volume: 11 year: 2019 ident: 10.1016/j.jpowsour.2025.237920_b38 article-title: Accurate estimation of battery SOH and RUL based on a progressive LSTM with a time compensated entropy index publication-title: Annu. Conf. PHM Soc. – year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b13 – volume: 12 start-page: 129022 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b32 article-title: Prediction of state-of-health and remaining-useful-life of battery based on hybrid neural network model publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3455255 – year: 2017 ident: 10.1016/j.jpowsour.2025.237920_b61 article-title: Oxford battery degradation dataset 1 publication-title: Univ. Oxf. – volume: 9 start-page: 3 issue: 12 year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b6 article-title: Second-life batteries: A review on power grid applications, degradation mechanisms, and power electronics interface architectures publication-title: Batteries doi: 10.3390/batteries9120571 – year: 2016 ident: 10.1016/j.jpowsour.2025.237920_b21 – volume: 59 start-page: 83 year: 2021 ident: 10.1016/j.jpowsour.2025.237920_b48 article-title: A review of lithium-ion battery safety concerns: The issues, strategies, and testing standards publication-title: J. Energy Chem. doi: 10.1016/j.jechem.2020.10.017 – year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b8 – volume: 405 start-page: 18 year: 2018 ident: 10.1016/j.jpowsour.2025.237920_b23 article-title: Towards a smarter battery management system: A critical review on battery state of health monitoring methods publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.10.019 – year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b47 article-title: High-entropy electrolytes for lithium-ion batteries publication-title: ACS Energy Lett. doi: 10.1021/acsenergylett.4c01358 – year: 2017 ident: 10.1016/j.jpowsour.2025.237920_b67 article-title: Oxford battery degradation dataset 1 publication-title: Univ. Oxf. – volume: 369 start-page: 5 year: 2024 ident: 10.1016/j.jpowsour.2025.237920_b29 article-title: State of health (SoH) estimation methods for second life lithium-ion battery—Review and challenges publication-title: Appl. Energy – volume: 105 start-page: 2354 year: 2017 ident: 10.1016/j.jpowsour.2025.237920_b56 article-title: Real-time fault diagnosis method of battery system based on Shannon entropy publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.03.676 – volume: 341 start-page: 373 year: 2016 ident: 10.1016/j.jpowsour.2025.237920_b10 article-title: Degradation diagnostics for lithium ion cells publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.12.011 – year: 2010 ident: 10.1016/j.jpowsour.2025.237920_b42 – start-page: 52 year: 2009 ident: 10.1016/j.jpowsour.2025.237920_b57 article-title: Advances in feature selection with mutual information – year: 2023 ident: 10.1016/j.jpowsour.2025.237920_b1 – volume: 113 year: 2019 ident: 10.1016/j.jpowsour.2025.237920_b18 article-title: Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2019.109254 – volume: 34 start-page: 1 year: 2003 ident: 10.1016/j.jpowsour.2025.237920_b54 article-title: Notes on Landauer’s principle, reversible computation, and Maxwell’s Demon publication-title: Elsevier Sci. – year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b11 – volume: 8 start-page: 2205 issue: 2 year: 2022 ident: 10.1016/j.jpowsour.2025.237920_b19 article-title: A comprehensive review on the characteristics and modeling of lithium-ion battery aging publication-title: IEEE Trans. Transp. Electrification doi: 10.1109/TTE.2021.3138357 – volume: 4 start-page: 383 year: 2019 ident: 10.1016/j.jpowsour.2025.237920_b60 article-title: Data-driven prediction of battery cycle life before capacity degradation. publication-title: Nat. Energy doi: 10.1038/s41560-019-0356-8 – volume: 67 start-page: 319 year: 2018 ident: 10.1016/j.jpowsour.2025.237920_b16 article-title: Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2865664 – volume: 4 start-page: 310 year: 2020 ident: 10.1016/j.jpowsour.2025.237920_b17 article-title: Battery lifetime prognostics publication-title: Joule doi: 10.1016/j.joule.2019.11.018 – volume: 334–335 start-page: 73 year: 2004 ident: 10.1016/j.jpowsour.2025.237920_b64 article-title: The ARTEMIS European driving cycles for measuring car pollutant emissions publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2004.04.070 |
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