A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries

In this paper, machine learning (ML)/deep learning (DL) has been employed to predict the thermal runaway (TR) occurrence in Li-ion batteries (LIBs). State-of-the-art convolutional neural networks (CNNs) are adopted to forecast the evolution of the TR phenomenon with a classification approach of three distinct stages, namely, safe operation, critical condition of triggered the TR, and the actual TR occurrence. In addition, the "you look only once" (YOLO) object detection ML model is used to identify the location of the TR heat sources within the battery. The neural networks are trained on simulated thermal images, computed with the multiphysics modeling of LIBs. The multiphysics modeling approach comprises a coupled thermal, electrochemical (P2D model), and degradation sub-models. The degradation phenomenon leading to the TR considered in this paper is the solid electrolyte interface (SEI) formation/decomposition on the negative electrode. The proposed ML model exhibits high accuracy in predicting the TR evolution stages and heat source locations. The combined multiphysics and ML modeling approach developed in this work provides qualitative insights for 'on-the-fly' prediction of the TR, as well as a framework for extending data-driven methodologies to broad applications in electrochemistry.