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The rapid adoption of electric vehicles (EVs) necessitates a well-planned and resilient charging infrastructure to support widespread accessibility. This study presents a systematic, data-driven framework for optimal electric vehicle charging station (EVCS) site selection by integrating multi-criteria decision analysis (AHP & Fuzzy-AHP), spatial optimization using Voronoi diagrams , and sensitivity analysis. Key decision factors include accessibility, vehicle ownership density, proximity to points of interest and resilience to environmental hazards such as floods and wildfires. AHP and Fuzzy-AHP suitability maps are compared to identify areas of agreement and uncertainty, leading to an integrated suitability map that enhances decision-making confidence. A Voronoi-based spatial optimization approach is then employed to iteratively refine EVCS placement, ensuring fair coverage and minimizing redundancy. Results indicate that urban EVCS should be spaced every 6-10 km apart to accommodate high trip frequency, while rural areas require stations every 15-20 km, with additional placements at highway junctions to prevent accessibility gaps. The study also highlights the importance of a location specific charging mix, recommending Level 2 chargers for residential and workplace settings and DC Fast Chargers for highways and high-traffic areas. Beyond spatial optimization, this study emphasizes the importance of an adaptive, phased deployment strategy, where EVCS expansion is guided by real-world demand rather than static planning assumptions. The proposed framework is scalable and transferable, providing a replicable methodology for EVCS planning across diverse geographic regions. By bridging the gap between theoretical models and practical deployment, this study contributes to the development of a resilient and demand-responsive EV charging network.
Momin et al. (Thu,) studied this question.