Artificial Intelligence in Bidirectional Energy Management: A Critical Review of Control Strategies for V2G and G2V Systems

ABSTRACT The growing adoption of electric vehicles (EVs) and renewable energy sources has accelerated the need for intelligent bidirectional energy management systems. Vehicle‐to‐Grid (V2G) and Grid‐to‐Vehicle (G2V) technologies offer promising solutions to enhance grid stability, optimise energy use and leverage EVs as mobile storage units. However, real‐time control of these systems remains challenging due to the dynamic nature of power flows, user behaviour and energy demand uncertainties. This review explores how Artificial Intelligence (AI) and Machine Learning (ML) methods–such as supervised learning, reinforcement learning, deep learning and hybrid models–address these challenges. This paper highlight key applications, recent case studies, and experimental advances in AI‐driven V2G and G2V control, emphasising improvements in system efficiency, battery health and grid reliability. Critical challenges including data quality, real‐time computation, and cybersecurity are discussed, along with future directions such as Explainable AI, blockchain integration, and lightweight decentralised models. This review aims to present a critical overview of AI‐enabled control strategies for V2G and G2V systems and to guide future innovations in sustainable and intelligent energy management.