Optimizing EV Charging Recommendations Using Graph Neural Networks

Electric vehicles (EVs) offer low carbon emissions; however, drivers frequently encounter difficulties scheduling charging sessions and locating available Charging Stations (CSs). These stations not only need to be conveniently located but also tailored to meet individual charging preferences. Addressing these challenges is crucial for overcoming barriers to the widespread adoption and efficiency of electric mobility, consequently enhancing the overall user experience. This paper introduces an innovative two-stage framework that improves the accessibility of EVCSs by integrating Graph Neural Networks (GNNs) with optimization algorithms. A bipartite graph representing user-station interactions is constructed in the first stage, and a GNN is utilized to leverage this structure. The GNNs efficiently capture complex relational patterns within the graph, enabling the generation of personalized station recommendations. In the subsequent stage, an optimization algorithm is employed to strategically assign users to these recommended stations. This algorithm considers station availability and proximity factors, ensuring optimal user assignments. The proposed recommender framework’s effectiveness is verified using data collected from the Yonne department in France. Experimental evaluations highlight the framework’s efficiency, achieving a high-performance measure of 98%, significantly reducing waiting times and maximizing user satisfaction for drivers compared to baseline approaches.