AI-Driven Energy Management in Electric Vehicles: Recent Advances in Storage and Conversion Technologies

This Research presents a systematic review and AI-based comparative analysis of recent advances in energy management systems for electric vehicles (EVs), with a focus on storage and conversion technologies. The research investigates the role of artificial intelligence in optimizing battery performance, energy efficiency, charging strategies, and power conversion mechanisms in modern EV architectures. A comprehensive review of recent literature was conducted to identify emerging trends, challenges, and innovations in lithium-ion batteries, supercapacitors, hybrid energy storage systems, and bidirectional converters. NVivo was employed for qualitative thematic analysis to categorize research patterns and technological developments, while MATLAB was utilized for AI-driven comparative evaluation and performance analysis of different energy management models. The findings reveal that AI techniques, including machine learning and predictive optimization, significantly enhance energy utilization, battery lifespan, and real-time decision-making in EV systems. The research  highlights the growing importance of intelligent energy management in achieving sustainable, efficient, and high-performance electric mobility solutions.