Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Health monitoring, fault analysis, and detection methods are important to operate battery systems safely. We apply Gaussian process resistance models on lithium-iron-phosphate (LFP) battery field data to separate the time-dependent and operating-point-dependent resistances. The dataset contains 28 b...

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Bibliographic Details
Main Authors: Schaeffer, Joachim, Lenz, Eric, Gulla, Duncan, Bazant, Martin Z, Braatz, Richard D, Findeisen, Rolf
Other Authors: Massachusetts Institute of Technology. Department of Chemical Engineering
Format: Article
Language:English
Published: Elsevier BV 2024
Online Access:https://hdl.handle.net/1721.1/157659

Internet

https://hdl.handle.net/1721.1/157659

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