Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
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arXiv
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| Format: | Preprint |
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2024
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| author | Schaeffer, Joachim Galuppini, Giacomo Rhyu, Jinwook Asinger, Patrick A. Droop, Robin Findeisen, Rolf Braatz, Richard D. |
| author_facet | Schaeffer, Joachim Galuppini, Giacomo Rhyu, Jinwook Asinger, Patrick A. Droop, Robin Findeisen, Rolf Braatz, Richard D. |
| contents | Batteries are dynamic systems with complicated nonlinear aging, highly dependent on cell design, chemistry, manufacturing, and operational conditions. Prediction of battery cycle life and estimation of aging states is important to accelerate battery R&D, testing, and to further the understanding of how batteries degrade. Beyond testing, battery management systems rely on real-time models and onboard diagnostics and prognostics for safe operation. Estimating the state of health and remaining useful life of a battery is important to optimize performance and use resources optimally.
This tutorial begins with an overview of first-principles, machine learning, and hybrid battery models. Then, a typical pipeline for the development of interpretable machine learning models is explained and showcased for cycle life prediction from laboratory testing data. We highlight the challenges of machine learning models, motivating the incorporation of physics in hybrid modeling approaches, which are needed to decipher the aging trajectory of batteries but require more data and further work on the physics of battery degradation. The tutorial closes with a discussion on generalization and further research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04049 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More Schaeffer, Joachim Galuppini, Giacomo Rhyu, Jinwook Asinger, Patrick A. Droop, Robin Findeisen, Rolf Braatz, Richard D. Systems and Control Machine Learning Batteries are dynamic systems with complicated nonlinear aging, highly dependent on cell design, chemistry, manufacturing, and operational conditions. Prediction of battery cycle life and estimation of aging states is important to accelerate battery R&D, testing, and to further the understanding of how batteries degrade. Beyond testing, battery management systems rely on real-time models and onboard diagnostics and prognostics for safe operation. Estimating the state of health and remaining useful life of a battery is important to optimize performance and use resources optimally. This tutorial begins with an overview of first-principles, machine learning, and hybrid battery models. Then, a typical pipeline for the development of interpretable machine learning models is explained and showcased for cycle life prediction from laboratory testing data. We highlight the challenges of machine learning models, motivating the incorporation of physics in hybrid modeling approaches, which are needed to decipher the aging trajectory of batteries but require more data and further work on the physics of battery degradation. The tutorial closes with a discussion on generalization and further research directions. |
| title | Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2404.04049 |