Integrating EV fast charging stations with traffic dynamics: A real-time traffic fluid model approach

Abstract

The Electric Vehicle (EV) has gained significant attraction in urban transportation, driven by the convergence of fast charging capabilities of battery and heightened environmental consciousness. Efficient operation of Fast Charging Stations (FCSs) requires addressing challenges such as the rapid and accurate processing of traffic condition data. This study presents a fluid model that leverages real-time traffic data to estimate FCS capacity, taking into account factors like vehicle density and speed in urban traffic segments. A rolling horizon scheduling approach is then employed to develop a simulated EV charging schedule based on the estimated FCS capacity. The simulation aims to maximize FCS utilization, ensure customer satisfaction, and respond to dynamic power demands. The study also examines the impact of the fluid model on operational efficiency under various traffic conditions and evaluates the proposed scheduling method across different time intervals. By integrating traffic dynamics into capacity estimation and utilization optimization, this research enhances strategies for effective FCS operations. Furthermore, it provides practical insights for optimizing EV charging station operations amidst evolving urban dynamics.