The Expansion Plan for Charging Stations Based on K-Medoids and Vehicle GPS Data

This study presents an expansion plan for charging stations based on the K-Medoids clustering algorithm and vehicle GPS data. The growing number of electric vehicles has led to a critical problem of insufficient capacity in existing charging stations. Through an analysis of vehicle GPS data and charging behavior, we determine the specific charging demands and time periods. By utilizing the K-Medoids algorithm and considering the current charging station locations, we cluster vehicles into groups that exhibit similar driving patterns and charging requirements. Consequently, we evaluate and increase the number of charging stations based on these clusters, with the objective of augmenting the charging station capacity and effectively catering to the diverse needs of different vehicle groups. This strategy ultimately optimizes the charging network's layout. The proposed approach serves as a valuable strategy for charging infrastructure operators in their efforts to plan charging station capacity, improve the efficiency and reliability of the charging network, and foster the widespread adoption and utilization of electric vehicles.