Electric Vehicle Charging

The field examines when and where vehicles charge, how to site and operate stations, and how charging affects drivers, fleets, and electricity networks. Common approaches include graph learning and probabilistic models for demand forecasting, optimization and reinforcement learning for routing and scheduling, and multi-agent control for coordinating stations with the grid. Researchers also study charging hardware, cyberattack detection, privacy-preserving data analysis, and synthetic data and benchmarks. A central challenge is balancing realistic user behavior and uncertain demand with computational tractability, while ensuring that learned decisions remain safe for the grid and useful across different settings.