The increasing prominence of autonomous electric vehicles (AEVs) necessitates advanced Battery Management Systems (BMS) capable of real-time optimization under dynamic conditions. Traditional BMS approaches often struggle with the complex, non-linear dynamics of battery behavior and the variable energy demands inherent in autonomous driving. This paper reviews the potential of Reinforcement Learning (RL) as a promising methodology for developing adaptive and efficient BMS for AEVs. It outlines the fundamental concepts of RL pertinent to this application, including state representation, action spaces, reward formulation, and relevant algorithms. The discussion explores how RL techniques can be applied to optimize critical BMS functions such as state estimation, thermal management, energy efficiency, and lifespan maximization in real time. By learning optimal control policies through interaction with the battery system and its environment, RL offers significant advantages over conventional, static control strategies. This review concludes that RL provides a robust framework for intelligent BMS design, contributing to the safety, reliability, and sustainability of future autonomous transportation systems.