Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929509815549952 |
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| author | Lanubile, Andrea Bosoni, Pietro Pozzato, Gabriele Allam, Anirudh Acquarone, Matteo Onori, Simona |
| author_facet | Lanubile, Andrea Bosoni, Pietro Pozzato, Gabriele Allam, Anirudh Acquarone, Matteo Onori, Simona |
| contents | Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14575 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries Lanubile, Andrea Bosoni, Pietro Pozzato, Gabriele Allam, Anirudh Acquarone, Matteo Onori, Simona Machine Learning Systems and Control Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% . |
| title | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2409.14575 |