Evaluation of Suitability Index Selection of Electric Vehicle Charging Stations Through MCDM Approaches

The rapid adoption of electric vehicles (EVs) necessitates the strategic development of charging infrastructure to support seamless mobility and sustainability goals. This study presents an integrated multi-criteria decision-making (MCDM) framework for the optimal selection of Electric Vehicle Charging Stations (EVCS) by combining Hellwig’s Method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The hybrid models—Hellwig’s-TOPSIS and TOPSIS enhanced with Mahalanobis distance—are applied to evaluate potential locations based on 14 diverse criteria that encompass technical, economic, environmental, and accessibility factors. The criteria include Daily Traffic Volume, EV Density in the Area, Peak Demand Times, Renewable Energy Integration, Revenue Potential, and Installation Cost, among others, with relative weights determined through a systematic approach. The Hellwig’s method is employed to handle factor scores and construct synthetic indicators, while the Mahalanobis distance enhances TOPSIS robustness by accounting for correlations among attributes. The results offer a comprehensive ranking of EVCS locations, ensuring effective decision-making support for urban planners and policymakers. This framework aids in optimizing resource allocation and maximizing socio-economic and environmental benefits associated with EV infrastructure deployment.