AI-Driven Optimization of Battery Management for Enhanced EV Efficiency

Abstract

In the rapidly evolving landscape of electric vehicles (EVs), optimizing battery performance remains a pivotal challenge. This study presents a comprehensive comparison between traditional battery management techniques and an AI-driven approach leveraging Convolutional Neural Networks (CNNs) for EVs. Our investigations focused on three primary metrics: prediction accuracy concerning the State of Charge (SoC) and battery health, potential battery life extension, and computational efficiency. Results unequivocally showed that the CNN-based model surpassed traditional methodologies in all examined metrics, with improved prediction accuracies, a significant increase in estimated battery lifespan, and reduced computational times. This research underscores the potential of integrating AI into EV battery management systems, promising not only enhanced battery performance but also signaling a paradigm shift towards a more sustainable, efficient, and reliable electric transportation era.