Deep Learning Approaches for Enhancing Battery Safety and Performance in Electric Vehicles

Qiyuan Zhang, Xiaoqing Huang

Abstract: The rapid growth of electric vehicles (EVs) has highlighted the need for innovative approaches to enhance battery safety and performance. This paper explores the integration of deep learning techniques to address these challenges. Specifically, Convolutional Neural Networks (CNNs) combined with Particle Swarm Optimization (PSO) are applied to predict and prevent safety failures in batteries, such as thermal runaway and degradation. Additionally, Fully Connected Neural Networks (FNNs) paired with Bayesian Optimization are used to optimize battery performance, focusing on improving battery management systems (BMS), charging cycles, and energy efficiency. The proposed methodology leverages the power of data-driven models to enhance the safety, reliability, and efficiency of EV batteries. We present a series of experiments and evaluations comparing deep learning-based models with traditional rule-based systems, showing significant improvements in key metrics such as accuracy, precision, and F1 score. This research demonstrates the transformative potential of deep learning in advancing battery technology and electric mobility, paving the way for safer, more efficient, and longer-lasting batteries for electric vehicles.

External IDs:dblp:journals/tce/ZhangH25