Abstract This paper performs a novel data-driven approach to optimize electric vehicle (EV)public charging. We translate the study area into a directed graph by partitioning it into discrete grids. A modified geographical PageRank (MGPR) model is developed to estimate EV charging demand, built upon trip origin–destination (OD)and social dimension features, and validated against real-world charging data.The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization. It is shown that MGPR can effectively quantify the EV charging demand with satisfactory accuracy. Optimized EV charging stations based on the CMCLP model can remedy the spatial mismatch between the EV demand and the existing charging station allocations. The developed methodological framework is highly generalizable and can be extended to other regions for EV charging demand estimation and optimal charging infrastructure siting.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Electric vehicle demand estimation and charging station allocation using urban informatics


    Beteiligte:
    Yi, Zhiyan (Autor:in) / Liu, Xiaoyue Cathy (Autor:in) / Wei, Ran (Autor:in)


    Erscheinungsdatum :

    2022-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Electric vehicle charging station

    DEBOER JOHN / COWANS JOHN QUENTIN / BIEDRZYCKI TIMOTHY | Europäisches Patentamt | 2018

    Freier Zugriff

    Electric Vehicle Charging Station

    ELLIS DAVID | Europäisches Patentamt | 2023

    Freier Zugriff

    ELECTRIC VEHICLE CHARGING STATION

    FREELING-WILKINSON OLIVIER | Europäisches Patentamt | 2021

    Freier Zugriff

    ELECTRIC VEHICLE CHARGING STATION

    BADGER II CHARLES EVERETT | Europäisches Patentamt | 2017

    Freier Zugriff