Spatial Analysis and Clustering of Electric Vehicle Density for Charging Infrastructure Planning

The global shift to electric vehicles (EVs) to combat climate change is accelerating. To support the growing EV market, it is crucial to identify areas with high EV density for optimal charging station placement, load management, and grid stability. In this study, the K-Means clustering algorithm is employed to analyze the spatial distribution of EVs, utilizing features such as Longitude, Latitude, Altitude, Grid Availability, Population, and Income. Clustering is performed through feature scaling and Min-Max normalization, assigning cluster scores based on feature values. The study employs a fundamental clustering approach, focusing on a critical feature to map the Spatial Distribution of EV distribution in specific regions (Uttarakhand) probabilistically. The findings of this study offer valuable insights for policymakers and urban planners. A cleaner transportation ecosystem can be promoted by strategically placing charging stations in high-demand areas and encouraging EV adoption in underserved regions. Furthermore, our methodology can be expanded to incorporate additional parameters and real-time data, enhancing future EV infrastructure planning.