Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908343454400512 |
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| author | Rhyu, Jinwook Schaeffer, Joachim Li, Michael L. Cui, Xiao Chueh, William C. Bazant, Martin Z. Braatz, Richard D. |
| author_facet | Rhyu, Jinwook Schaeffer, Joachim Li, Michael L. Cui, Xiao Chueh, William C. Bazant, Martin Z. Braatz, Richard D. |
| contents | Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation Rhyu, Jinwook Schaeffer, Joachim Li, Michael L. Cui, Xiao Chueh, William C. Bazant, Martin Z. Braatz, Richard D. Machine Learning Applications 15-04 I.2.6 Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding. |
| title | Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation |
| topic | Machine Learning Applications 15-04 I.2.6 |
| url | https://arxiv.org/abs/2410.07458 |