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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.05326 |
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| _version_ | 1866908361055797248 |
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| author | Sours, Tyler Agarwal, Shivang Cormier, Marc Crivelli-Decker, Jordan Ridderbusch, Steffen Glazier, Stephen L. Aiken, Connor P. Singh, Aayush R. Xiao, Ang Allam, Omar |
| author_facet | Sours, Tyler Agarwal, Shivang Cormier, Marc Crivelli-Decker, Jordan Ridderbusch, Steffen Glazier, Stephen L. Aiken, Connor P. Singh, Aayush R. Xiao, Ang Allam, Omar |
| contents | Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers Sours, Tyler Agarwal, Shivang Cormier, Marc Crivelli-Decker, Jordan Ridderbusch, Steffen Glazier, Stephen L. Aiken, Connor P. Singh, Aayush R. Xiao, Ang Allam, Omar Machine Learning Materials Science Predicting the end-of-life (EOL) of lithium-ion batteries across different manufacturers presents significant challenges due to variations in electrode materials, manufacturing processes, cell formats, and a lack of generally available data. Methods that construct features solely on voltage-capacity profile data typically fail to generalize across cell chemistries. This study introduces a methodology that combines traditional voltage-capacity features with Direct Current Internal Resistance (DCIR) measurements, enabling more accurate and generalizable EOL predictions. The use of early-cycle DCIR data captures critical degradation mechanisms related to internal resistance growth, enhancing model robustness. Models are shown to successfully predict the number of cycles to EOL for unseen manufacturers of varied electrode composition with a mean absolute error (MAE) of 150 cycles. This cross-manufacturer generalizability reduces the need for extensive new data collection and retraining, enabling manufacturers to optimize new battery designs using existing datasets. Additionally, a novel DCIR-compatible dataset is released as part of ongoing efforts to enrich the growing ecosystem of cycling data and accelerate battery materials development. |
| title | Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life Predictions Across Manufacturers |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2410.05326 |