A Robust and Reliable Data-Driven Approach for Optimizing the Location of Electric Vehicle Charging Infrastructure

The strategic placement of Electric Vehicle (EV) charging stations is crucial for supporting the rapid adoption of EVs while ensuring efficient energy distribution and grid stability. This research presents a data-driven optimization framework that leverages multiple datasets, including EV consumption patterns, air quality indices, geospatial information, population density, road networks, and land use, to determine optimal locations for EV charging stations. We employ advanced clustering techniques such as K-means and DBSCAN to identify high-demand regions, coupled with a priority ranking mechanism incorporating accessibility, energy efficiency, and urban planning constraints. By systematically analyzing spatial and temporal demand variations, our approach ensures equitable charging station distribution while minimizing redundant infrastructure and operational costs. Experimental results demonstrate that clustering-based placement significantly outperforms traditional random placement methods regarding average distance to demand centers, grid stability, and traffic accessibility. The proposed methodology provides a scalable, cost-efficient, and sustainable solution for EV infrastructure planning, paving the way for smarter urban mobility and enhanced energy management in smart cities.