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Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
April 8, 2024, 4:42 a.m. | Joachim Schaeffer, Giacomo Galuppini, Jinwook Rhyu, Patrick A. Asinger, Robin Droop, Rolf Findeisen, Richard D. Braatz
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract aging arxiv batteries battery beyond chemistry cs.lg cs.sy design dynamic eess.sy life lithium lithium-ion batteries machine machine learning manufacturing prediction systems testing type understanding
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