Chemomechanical damage prediction from phase-field simulation video sequences using a deep-learning-based methodology
Date
2024-09-12
Journal Title
Journal ISSN
Volume Title
Publisher
iScience
Abstract
Highlights
• Deep-learning-based method to predict chemo-mechanical processes in electrode materials
• Prognostication of crack development and propagation based on machine learning
• Potential to integrate in battery management systems of large-format batteries
Summary
Understanding the failure mechanisms of lithium-ion batteries is essential for their greater adoption in diverse formats. Operando X-ray and electron microscopy enable the evaluation of concentration, phase, and stress heterogeneities in electrode architectures. Phase-field models are commonly used to capture multi-physics coupling including the interplay between electrochemistry and mechanics. However, very little has been explored regarding developing predictive models that would forecast imminent failure. This study explores the application of convolutional long short-term memory networks for damage prediction in cathode materials using video sequence from phase-field simulations as a proxy for video microscopy. Two models are examined making use of, respectively, the damage video only and the damage and hydrostatic stress videos combined. We use customized quantitative metrics to compare the performance of the models. Our work demonstrates the outstanding capability of deep learning models using limited data to predict fracture behavior of battery materials, including crack propagation angle and length.
Graphical abstract available at: https://doi.org/10.1016/j.isci.2024.110822
Description
This article was originally published in iScience. The version of record is available at: https://doi.org/10.1016/j.isci.2024.110822.
© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords
Citation
Zeng, Quan, Shahed Rezaei, Luis Carrillo, Rachel Davidson, Bai-Xiang Xu, Sarbajit Banerjee, and Yu Ding. “Chemomechanical Damage Prediction from Phase-Field Simulation Video Sequences Using a Deep-Learning-Based Methodology.” iScience 27, no. 9 (September 20, 2024): 110822. https://doi.org/10.1016/j.isci.2024.110822.