AI-Driven Optimization of V2G Systems for Electric School Buses

This paper explores the transformative potential of AI-driven optimization in Vehicle-to-Grid (V2G) systems, with a focus on electric school buses. V2G technology enables bidirectional energy flow, allowing electric vehicles to not only draw power from the grid but also discharge electricity back to it. This capability is particularly beneficial for electric school buses, which can serve as valuable energy storage assets during their idle periods. By integrating AI, these systems can enhance operational efficiency, reduce costs, and support environmental sustainability through optimized energy management. Despite these advantages, challenges such as technological complexities and infrastructure limitations persist. Addressing these challenges through targeted research and supportive policy frameworks is crucial for realizing the full potential of AI-driven V2G systems in advancing sustainable transportation and energy solutions.