Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries

Swift Prediction of Battery Performance: Applying Machine Learning Models on Microstructural Electrode Images for Lithium-Ion Batteries

In this study, we investigate the use of artificial neural networks as a potentially efficient method to determine the rate capability of electrodes for lithium-ion batteries with different porosities. The performance of a lithium-ion battery is, to a large extent, determined by the microstructure (...

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Bibliographic Details
Main Authors: Patrick Deeg, Christian Weisenberger, Jonas Oehm, Denny Schmidt, Orsolya Csiszar, Volker Knoblauch
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Batteries
Subjects:
CNN
deep learning
machine learning
image regression
lithium-ion batteries
Online Access:https://www.mdpi.com/2313-0105/10/3/99

Internet

https://www.mdpi.com/2313-0105/10/3/99

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