Enhancing battery pack safety against cone impact using machine learning techniques and Gaussian noise

Abstract

The increasing prevalence of electric vehicles underscores the need for enhanced battery pack safety, particularly against impacts that can lead to thermal runaway and fires. This study investigates the mechanical and thermal characteristics of battery packs subjected to cone impact on the lower part. Utilizing finite element model (FEM) simulations, we examined the effects of top radius, shell condition, velocity, and angle on the battery pack's response. Five machine learning (ML) techniques were employed to predict the battery pack's behavior under impact, with training data generated from a well-planned Latin hypercube experiment based on FEM dynamic simulations. The accuracy and robustness of the ML models were evaluated under various scenarios, including the introduction of Gaussian noise. Among the models tested, BESA-ELMM (Bald eagle search algorithm-Extreme learning machine model) demonstrated exceptional speed, making it suitable for real-time assessments, while WOA-SVMM (whale optimization algorithm-Support vector machine model) exhibited superior resilience and accuracy, particularly under noisy conditions. Both models, along with the other ML techniques, showed significant effectiveness in predicting the mechanical responses of battery packs to impact. Our findings indicate that ML approaches are highly efficient in evaluating the mechanical effects on battery packs, providing crucial insights for designing safer and more durable battery packs. This study contributes to the advancement of battery safety by demonstrating the potential of integrating ML techniques with FEM simulations to enhance the resilience and impact resistance of battery packs in electric vehicles.