AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration

Abstract

Electric Vehicles (EVs) are transforming the transportation sector, and their integration with the grid is crucial for a sustainable energy future. EVs can serve as distributed energy resources, aiding in peak shaving, frequency management, and voltage support, thus enhancing grid stability. This comprehensive review explores the transformative potential of EVs in the power grid, focusing on Vehicle-to-Grid (V2 G) technology. We discuss different bidirectional Converter types, including AC-DC and DC-DC converters, to optimize power flow and voltage regulation. AC-DC converters rectify AC grid power for DC charging, while DC-DC converters optimize DC power flow and voltage regulation. Charging station safety is paramount, with electrical shock protection, fire protection, and cybersecurity measures essential for ensuring safe and reliable charging. The review also delves into energy trading and security in blockchain management, highlighting the use of blockchain technology to address hacking vulnerabilities. We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. By leveraging AI and ML, we can improve the efficiency, reliability, and scalability of V2 G systems. AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. By integrating AI and ML into V2 G systems, we can unlock new possibilities for sustainable energy management, grid resilience, and electric vehicle adoption.