Large-Scale Optimization of Electric Vehicle Charging Infrastructure

The rapid adoption of electric vehicles (EVs) is driving increasing demand for efficient and strategically placed charging stations. While numerous studies have explored optimization methods for the placement of EV charging stations, most focus on smaller geographic areas, leaving the challenge of optimizing station distribution across larger regions unresolved. This paper presents a novel approach for optimizing both the placement and capacity of EV charging stations using the H3 spatial grid system and queuing theory. By leveraging the hexagonal structure of the H3 grid, we accurately model spatial data and analyze EV charging demands in both urban and non-urban areas. Queuing theory is employed to predict station utilization and optimize the allocation of charging points, minimizing user wait times and ensuring efficient resource distribution. The proposed method is adaptable to future growth in EV adoption and addresses infrastructure needs in both high-demand and underserved regions. This paper outlines the framework developed for the 13th SIGSPATIAL Cup (GISCUP 2024), which achieved top-5 performance. Results based on real-world data demonstrate the model’s effectiveness in enhancing the spatial distribution of charging stations, improving accessibility and efficiency in EV infrastructure.