Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
Name
2404.04049v1.pdf
Description
Accepted version
Size
1.82 MB
Format
Adobe PDF
Checksum (MD5)
2ac26c09784d09f73af4e80fe044a849
Author(s)
Schaeffer, Joachim
Galuppini, Giacomo
Rhyu, Jinwook
Asinger, Patrick A
Droop, Robin
Findeisen, Rolf
Braatz, Richard D
Date Issued
July 10, 2024
Journal
2024 American Control Conference (ACC)
Publisher
IEEE
Citation
J. Schaeffer et al., "Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More," 2024 American Control Conference (ACC), Toronto, ON, Canada, 2024, pp. 763-768.
Version
Author's final manuscript
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 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.
Description
2024 American Control Conference (ACC) July 8-12, 2024. Toronto, Canada
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/acc60939.2024.10644790
