Highlights Collective vehicle parking pattern reveals public charging demand. Siting charging stations based on travel patterns can improve electrification rate. Environmental impacts are assessed for different charging infrastructure siting scenarios. Peak vehicle charging demand overlaps with Beijing’s peak load.

    Abstract Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that (1) collective vehicle parking “hotspots” are good indicators for charging demand; (2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; (3) with current grid mix, emissions of CO2, PM, SO2, and NOx will increase with taxi electrification; and (4) power demand for public taxi charging has peak load around noon, overlapping with Beijing’s summer peak power.


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    Titel :

    Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet


    Beteiligte:
    Cai, Hua (Autor:in) / Jia, Xiaoping (Autor:in) / Chiu, Anthony S.F. (Autor:in) / Hu, Xiaojun (Autor:in) / Xu, Ming (Autor:in)


    Erscheinungsdatum :

    2014-01-01


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch