Modeling and Analysis of a Fast Charging Station and Evaluation of Service Quality for Electric Vehicles

Abstract

The deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations’ loading and customer service quality. Furthermore, the relationship between the initial investment decision on building certain number of ports and customer satisfaction should be quantified. This paper aims to analyze these aspects using one million vehicle days of travel data within the Columbus, OH, USA, region. Monte Carlo analysis is carried out in three types of areas—urban, suburban, and rural—to quantify the effect of uncertain parameters on DCFC station loading and service quality. Additional simulations based on a homogeneous vehicle population are carried out, and closed-form equations are derived therefrom to estimate charging duration and waiting time in the queue. Optimization of DCFC station design is also addressed through the number and capacity of ports.