Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions
Single-value prediction such as the End of Life and Remaining Useful Life is a common method of estimating the lifetime of Li-ion batteries. Information from such prediction is limited when the entire degradation pattern is needed for practical applications such as dynamic adjustment of battery warr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001319 |
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| author | Rasheed Ibraheem Timothy I. Cannings Torben Sell Gonçalo dos Reis |
| author_facet | Rasheed Ibraheem Timothy I. Cannings Torben Sell Gonçalo dos Reis |
| author_sort | Rasheed Ibraheem |
| collection | DOAJ |
| description | Single-value prediction such as the End of Life and Remaining Useful Life is a common method of estimating the lifetime of Li-ion batteries. Information from such prediction is limited when the entire degradation pattern is needed for practical applications such as dynamic adjustment of battery warranty, improved maintenance scheduling, and battery stock management. In this research, a predictive, semi-parametric survival model called the Cox Proportional Hazards is proposed for the prediction of cell degradation in the form of survival probability (battery reliability) and cumulative hazard (battery risk) functions. Once this model is trained, the two functions can be obtained directly for a new cell without having to predict several cogent points. The model is trained on the first 50 cycles of only the voltage profile from either the charge or discharge data regime, implying that our methodology is data region agnostic. The signature method with both desirable mathematical and machine learning properties was adopted as a feature extraction technique.The developed models are tested rigorously using application-driven strategies involving model robustness to the number of cycles of data required for model training and prediction, different fractions of training samples, and systematic data sparsity. The codes for modeling and testing are publicly available. |
| format | Article |
| id | doaj-art-9067a38684934fe786e8771aa554e9a7 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-9067a38684934fe786e8771aa554e9a72025-01-27T04:22:21ZengElsevierEnergy and AI2666-54682025-01-0119100465Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functionsRasheed Ibraheem0Timothy I. Cannings1Torben Sell2Gonçalo dos Reis3Maxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United KingdomMaxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United KingdomMaxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United KingdomMaxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United Kingdom; Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology (NOVA FCT), Caparica, 2829-516, Portugal; Corresponding author at: Maxwell Institute for Mathematical Sciences, School of Mathematics, University of Edinburgh, The Kings buildings, Edinburgh, EH9 3JF, Scotland, United Kingdom.Single-value prediction such as the End of Life and Remaining Useful Life is a common method of estimating the lifetime of Li-ion batteries. Information from such prediction is limited when the entire degradation pattern is needed for practical applications such as dynamic adjustment of battery warranty, improved maintenance scheduling, and battery stock management. In this research, a predictive, semi-parametric survival model called the Cox Proportional Hazards is proposed for the prediction of cell degradation in the form of survival probability (battery reliability) and cumulative hazard (battery risk) functions. Once this model is trained, the two functions can be obtained directly for a new cell without having to predict several cogent points. The model is trained on the first 50 cycles of only the voltage profile from either the charge or discharge data regime, implying that our methodology is data region agnostic. The signature method with both desirable mathematical and machine learning properties was adopted as a feature extraction technique.The developed models are tested rigorously using application-driven strategies involving model robustness to the number of cycles of data required for model training and prediction, different fractions of training samples, and systematic data sparsity. The codes for modeling and testing are publicly available.http://www.sciencedirect.com/science/article/pii/S2666546824001319Battery degradationCox Proportional HazardsPath signature methodologySurvival probability functionCumulative hazard functionSurvival analysis |
| spellingShingle | Rasheed Ibraheem Timothy I. Cannings Torben Sell Gonçalo dos Reis Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions Energy and AI Battery degradation Cox Proportional Hazards Path signature methodology Survival probability function Cumulative hazard function Survival analysis |
| title | Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions |
| title_full | Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions |
| title_fullStr | Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions |
| title_full_unstemmed | Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions |
| title_short | Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions |
| title_sort | robust survival model for the prediction of li ion battery lifetime reliability and risk functions |
| topic | Battery degradation Cox Proportional Hazards Path signature methodology Survival probability function Cumulative hazard function Survival analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001319 |
| work_keys_str_mv | AT rasheedibraheem robustsurvivalmodelforthepredictionofliionbatterylifetimereliabilityandriskfunctions AT timothyicannings robustsurvivalmodelforthepredictionofliionbatterylifetimereliabilityandriskfunctions AT torbensell robustsurvivalmodelforthepredictionofliionbatterylifetimereliabilityandriskfunctions AT goncalodosreis robustsurvivalmodelforthepredictionofliionbatterylifetimereliabilityandriskfunctions |