  Recharging a plug-in electric vehicle is more time-consuming than refueling
an internal combustion engine vehicle. As a result, charging stations may face
serious congestion problems during peak traffic hours in the near future with
the rapid growth of plug-in electric vehicle population. Considering that
drivers' time costs are usually expensive, charging congestion will be a
dominant factor that affect a charging station's quality of service. Hence, it
is indispensable to conduct adequate congestion analysis when designing
charging stations in order to guarantee acceptable quality of service in the
future. This paper proposes a data-driven approach for charging congestion
analysis of plug-in electric vehicle charging stations. Based on a data-driven
plug-in electric vehicle charging station planning model, we adopt the queuing
theory to model and analyze the charging congestion phenomenon in these
planning results. We simulate and analyze the proposed method for charging
stations servicing shared-use electric taxis in the central area of Beijing
leveraging real-world taxi travel data.
