Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries
Abstract 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 st...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-024-00304-2 |
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| _version_ | 1846165179764572160 |
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| author | Andrea Lanubile Pietro Bosoni Gabriele Pozzato Anirudh Allam Matteo Acquarone Simona Onori |
| author_facet | Andrea Lanubile Pietro Bosoni Gabriele Pozzato Anirudh Allam Matteo Acquarone Simona Onori |
| author_sort | Andrea Lanubile |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-433453a6d3f34a3695136253a0bcef9e |
| institution | Kabale University |
| issn | 2731-3395 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-433453a6d3f34a3695136253a0bcef9e2024-11-17T12:30:59ZengNature PortfolioCommunications Engineering2731-33952024-11-013111310.1038/s44172-024-00304-2Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteriesAndrea Lanubile0Pietro Bosoni1Gabriele Pozzato2Anirudh Allam3Matteo Acquarone4Simona Onori5Energy Science & Engineering, Stanford UniversityEnergy Science & Engineering, Stanford UniversityEnergy Science & Engineering, Stanford UniversityEnergy Science & Engineering, Stanford UniversityEnergy Department, Politecnico di TorinoEnergy Science & Engineering, Stanford UniversityAbstract 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%.https://doi.org/10.1038/s44172-024-00304-2 |
| spellingShingle | Andrea Lanubile Pietro Bosoni Gabriele Pozzato Anirudh Allam Matteo Acquarone Simona Onori Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries Communications Engineering |
| title | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| title_full | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| title_fullStr | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| title_full_unstemmed | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| title_short | Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries |
| title_sort | domain knowledge guided machine learning framework for state of health estimation in lithium ion batteries |
| url | https://doi.org/10.1038/s44172-024-00304-2 |
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