Learning-Driven Optimization with Integrated Multi-Model Approaches for Efficient Electric Vehicle Charging Scheduling

Electric vehicles (EVs) are pivotal to reducing greenhouse gas emissions, yet their growing adoption presents major challenges for power grid management. This paper addresses the NP-hard problem of scheduling EV charging at public stations, considering vehicles’ arrival/departure times and user-defined charging demands. Under power and charger constraints, the goal is to minimize the total deviation between requested and achieved state-of-charge levels. We propose a hybrid optimization framework that integrates a genetic algorithm with mathematical programming. To reduce computational cost, we further introduce a multi-surrogate-assisted approach that approximates the mathematical model. Simulation results demonstrate the effectiveness and efficiency of the proposed methods in solving large-scale EV charging scheduling problems.