PRIORITIZING THE CRITERIA OF SELECTION FOR DEPLOYMENT OF ELECTRIC VEHICLE CHARGING STATIONS

Abstract

The rapid adoption of electric vehicles (EVs) has increased the demand for strategically placed charging stations to ensure seamless mobility and enhance user satisfaction. This study aims to prioritize the critical criteria for the selection of EV charging station deployment sites using three robust Multi-Criteria Decision-Making (MCDM) techniques. PCA method is implemented to reduces dimensionality and identify the most influential criteria, and highlights patterns in the dataset. CRITIC (Criteria Importance Through Intercriteria Correlation) and Entropy methods are developed to prioritize the criteria based on the loadings of the principal components.
The research identifies key criteria, such as location accessibility, energy availability, proximity to demand centers, land cost, environmental impact, and infrastructure feasibility, which influence the optimal placement of EV charging stations. The CRITIC method evaluates the criteria's importance by considering both the contrast intensity and inter-criteria conflict, thereby providing objective weights. The Entropy method quantifies the inherent uncertainty and diversity in the data to derive criterion weights. PCA reduces dimensionality, identifies the most influential criteria, and highlights patterns in the dataset.
The comparative analysis of results derived from these methods ensures robustness in decision-making, offering insights into the alignment and discrepancies among the weighting techniques. The findings provide stakeholders, including urban planners, policymakers, and investors, with a structured framework for making data-driven decisions. This study contributes to the efficient deployment of EV charging infrastructure, supporting sustainable urban mobility and advancing the transition toward greener transportation solutions.