A deep-learning approach to parameter fitting for a lithium metal battery cycling model, validated with experimental cell cycling time series

A deep-learning approach to parameter fitting for a lithium metal battery cycling model, validated with experimental cell cycling time series

Abstract Symmetric coin cell cycling is an important tool for the analysis of battery materials, enabling the study of electrode/electrolyte systems under realistic operating conditions. In the case of metal lithium SEI growth and shape changes, cycling studies are especially important to assess the...

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Bibliographic Details
Main Authors: Maria Grazia Quarta, Ivonne Sgura, Elisa Emanuele, Jacopo Strada, Raquel Barreira, Benedetto Bozzini
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Battery PDE modelling
Parameter estimation
CNN-LSTM neural networks
Lithium metal
Symmetric cell
Galvanostatic cycling
Online Access:https://doi.org/10.1038/s41598-025-87830-x
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https://doi.org/10.1038/s41598-025-87830-x

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