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Journal Articles

Mechanical Behavior and Failure Prediction of Cylindrical Lithium-Ion Batteries Under Mechanical Abuse Using Data-Driven Machine Learning

Xin-chun Zhang, Li-rong Gu, Xiao-di Yin, Zi-xuan Huang, Tie-jun Ci, Li-xiang Rao, Qing-long Wang, Marwan El-Rich
Journal: Journal of Applied Mechanics
Publisher: ASME
Article Type: Research Papers
J. Appl. Mech. February 2025, 92(2): 021003.
Paper No: JAM-24-1295
https://doi.org/10.1115/1.4067254
Published Online: December 16, 2024
...-driven application of machine learning (ML) models to rapidly predict the mechanical behavior and failure of cylindrical cells under different loading conditions. Mechanical abuse experiments including local indentation, flat compression, and three-point bending experiments were conducted on cylindrical...
View Article titled, Mechanical Behavior and Failure Prediction of Cylindrical Lithium-Ion Batteries Under <span class="search-highlight">Mechanical</span> <span class="search-highlight">Abuse</span> Using Data-Driven Machine Learning
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Topics: Artificial neural networks, Batteries, Compression, Failure, Lithium-ion batteries, Machine learning, Mechanical behavior, Temperature, Finite element model, Displacement
Includes: Supplementary data