Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917478439845888 |
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| author | Xue, Jingyuan Zhao, Xiaozhen Jiang, Dongjing Jiao, Qingchong Bouchtaoui, Redouane EL Zhang, Jianfei |
| author_facet | Xue, Jingyuan Zhao, Xiaozhen Jiang, Dongjing Jiao, Qingchong Bouchtaoui, Redouane EL Zhang, Jianfei |
| contents | Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for optimizing maintenance schedules, reducing costs, and improving safety. Traditional RUL prediction methods often struggle with nonlinear degradation patterns and uncertainty quantification. To address these challenges, we propose a hybrid survival analysis framework integrating survival data reconstruction, survival model learning, and survival probability estimation. Our approach transforms battery voltage time series into time-to-failure data using path signatures. The multiple Cox-based survival models and machine-learning-based methods, such as DeepHit and MTLR, are learned to predict battery failure-free probabilities over time. Experiments conducted on the Toyota battery and NASA battery datasets demonstrate the effectiveness of our approach, achieving high time-dependent AUC and concordance index (C-Index) while maintaining a low integrated Brier score. The data and source codes are available to the public at https://github.com/okic-ca/rul |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life Xue, Jingyuan Zhao, Xiaozhen Jiang, Dongjing Jiao, Qingchong Bouchtaoui, Redouane EL Zhang, Jianfei Signal Processing Artificial Intelligence Machine Learning Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for optimizing maintenance schedules, reducing costs, and improving safety. Traditional RUL prediction methods often struggle with nonlinear degradation patterns and uncertainty quantification. To address these challenges, we propose a hybrid survival analysis framework integrating survival data reconstruction, survival model learning, and survival probability estimation. Our approach transforms battery voltage time series into time-to-failure data using path signatures. The multiple Cox-based survival models and machine-learning-based methods, such as DeepHit and MTLR, are learned to predict battery failure-free probabilities over time. Experiments conducted on the Toyota battery and NASA battery datasets demonstrate the effectiveness of our approach, achieving high time-dependent AUC and concordance index (C-Index) while maintaining a low integrated Brier score. The data and source codes are available to the public at https://github.com/okic-ca/rul |
| title | Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.13558 |