Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance

Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this p...

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Bibliographic Details

Published in:	Energy and AI
Main Authors:	Akihiro Yamashita, Sascha Berg, Egbert Figgemeier
Format:	Article
Language:	English
Published:	Elsevier 2024-09-01
Subjects:	Unsupervised learning Dimensionality reduction Inductive bias Machine learning Variational autoencoder
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666546824000752