Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909601564196864 |
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| author | Nicolae, Constantin-Daniel Sameer, Sara Sun, Nathan Yan, Karena |
| author_facet | Nicolae, Constantin-Daniel Sameer, Sara Sun, Nathan Yan, Karena |
| contents | Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model Nicolae, Constantin-Daniel Sameer, Sara Sun, Nathan Yan, Karena Machine Learning Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition. |
| title | Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.17174 |