Advancements in Li-Ion Battery Safety: A Multiphysics and Deep Learning Approach for Thermal Runaway Prediction

The increasing adoption of Lithium-ion batteries (LIBs) in various applications necessitates an enhanced focus on safety, particularly concerning the thermal runaway (TR) phenomenon. This study introduces a novel framework combining multiphysics modeling and deep learning (DL) to predict TR in cylindrical Li-ion batteries. The primary objective is to prevent catastrophic battery failures by identifying critical thermal conditions leading to TR. Our methodology integrates state-of-the-art convolutional neural networks (CNNs) and the "You Only Look Once" (YOLO) object detection model to classify and detect the evolution of TR stages in LIBs. The CNNs, trained on simulated thermal images generated through comprehensive multiphysics modeling, classify TR into three distinct stages: safe operation, critical condition, and actual TR occurrence. The YOLO model further pinpoints the location of TR heat sources, enhancing the predictive accuracy. The multiphysics model employed captures the coupled thermal and electrochemical behavior of LIBs, focusing on the solid electrolyte interface (SEI) formation/decomposition on the anode as the primary degradation mechanism. The simulated thermal imagery, reflecting real-time battery conditions, serves as a dataset for training our DL models. This combination of physics-based and data-driven approaches allows for a detailed understanding of TR dynamics. In our study, we also explored the efficiency of other neural network architectures, notably ResNet, EfficientNet, and MobileNet for their potential in TR prediction. ResNet, known for its deep residual learning framework, demonstrated proficiency in handling the complex, layered features of thermal images. EfficientNet, optimized for efficiency and accuracy, showed promise in its scalable architecture and balanced depth, width, and resolution, making it a viable alternative for real-time TR detection. The MobileNet known for its lightweight architecture and efficiency on mobile devices, was evaluated for its capability to process thermal images in a resource-constrained environment. It demonstrated a remarkable balance between computational efficiency and predictive accuracy, making it a viable option for real-time TR monitoring in portable LIB applications. While MobileNet, ResNet, and EfficientNet each showed potential in their respective capacities, our primary CNN-YOLO framework emerged as the most effective for TR prediction in this study. Its proficiency in extracting spatial features and detecting anomalies in real-time was critical for early TR identification in LIBs. Results demonstrate high accuracy in predicting TR stages and heat source locations, emphasizing the ability of the integrated approach. The study highlights the importance of thermal management and real-time monitoring in LIBs, offering insights into the thermal behavior under various load conditions. The predictive framework not only advances the fundamental understanding of TR in LIBs but also provides practical implications for enhancing battery safety in applications like electric vehicles and consumer electronics. In conclusion, this work presents a significant stride in battery research, paving the way for safer and more reliable energy storage solutions. The combined multiphysics modeling and DL framework offer a robust tool for predicting and preventing TR in LIBs, contributing to the broader goal of advancing battery technology in the modern energy landscape.