English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recharging a plug-in electric vehicle is more time-consuming than refuelingan internal combustion engine vehicle. As a result, charging stations may faceserious congestion problems during peak traffic hours in the near future withthe rapid growth of plug-in electric vehicle population. Considering thatdrivers' time costs are usually expensive, charging congestion will be adominant factor that affect a charging station's quality of service. Hence, itis indispensable to conduct adequate congestion analysis when designingcharging stations in order to guarantee acceptable quality of service in thefuture. This paper proposes a data-driven approach for charging congestionanalysis of plug-in electric vehicle charging stations. Based on a data-drivenplug-in electric vehicle charging station planning model, we adopt the queuingtheory to model and analyze the charging congestion phenomenon in theseplanning results. We simulate and analyze the proposed method for chargingstations servicing shared-use electric taxis in the central area of Beijingleveraging real-world taxi travel data.

Plug-in Electric Vehicle Charging Congestion Analysis Using Taxi Travel Data in the Central Area of Beijing