From Electrochemical Analysis to Machine Learning Prediction: A Comprehensive Approach to LIB Safety
Authors: AbdiSobbouhi Yasaman , Das Goswami Basab Ranjan , Mashayek Farzad , Kingston Todd A. , Yurkiv Vitaliy 
Published: December 2024

The advancement of lithium-ion battery (LIB) technology has been pivotal in powering a wide range of applications, from portable electronics to electric vehicles (EVs). However, the safety of LIBs remains a significant concern, primarily due to the risk of thermal runaway (TR), a process where an increase in temperature leads to a self-sustaining chain reaction resulting in catastrophic failure. Our study introduces a groundbreaking framework that integrates multiphysics modeling with machine learning (ML) to predict the onset of TR in LIB modules, thereby advancing battery safety and reliability.
The multiphysics model developed in this research incorporates the comprehensive electrochemical and thermal behavior of LIBs, with a particular focus on the degradation mechanisms that lead to TR. This includes a detailed analysis of the breakdown of solid electrolyte interface (SEI) and its impact on battery stability. The model simulates various operational scenarios, such as constant charge/discharge and dynamic driving cycles, to replicate real-world battery usage. The thermal, electrochemical, and mechanical stresses are computed, enabling the identification of conditions that cause TR. In parallel, we have implemented an ML framework utilizing a hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) algorithm. This innovative approach leverages the spatio-temporal data generated from the multiphysics simulations, effectively predicting temperature evolution across the battery module. The GNN component captures spatial dependencies related to the battery's internal structure and thermal pathways, while the LSTM network models the temporal dynamics of the battery's thermal response. This dual mechanism ensures a comprehensive analysis of the potential hotspots and thermal anomalies that precede TR events.
The model's accuracy in representing the physical phenomena was validated against experimental discharge data at various C-rates, showing a high correlation with real-world battery behavior. One key finding was the identification of temperature thresholds where SEI decomposition accelerates, leading to rapid temperature increases. The model also highlighted the impact of operational stress, such as high C-rates, on accelerating degradation and advancing the onset of TR. Our findings reveal that the integrated multiphysics-ML model can accurately forecast critical thermal events in LIBs, offering a substantial lead time before the onset of TR. This predictive capability enables proactive measures to be taken, such as system shutdown or cooling interventions, thus averting potential damage and enhancing battery safety. Moreover, the model's adaptability to various battery configurations and operational conditions signifies a significant leap toward battery safety solutions.
VY and TAK gratefully acknowledge financial support from the Department of Defense (DoD) Office of Naval Research (ONR) through the Defense Established Program to Stimulate Competitive Research (DEPSCoR).