Abstract The growing use of electric vehicles (EVs) promotes environmental protection and energy conservation. The prerequisite in the use of EVs is that they should be adequately charged. The layout planning of charging station locations is therefore a key point in meeting the charging demands of EVs. This study presents three types of charging demands (i.e., conventional charging, fast charging, and fast battery replacement demand) by forecasting electric vehicle ownership with the use of the Bass model based on traditional vehicle development. This model for locating charging stations is built and optimized on the basis of the forecasted charging demands. The aim is to minimize the layout construction cost for charging station locations and the charging cost for customers. A practical example that applies the model to optimize the layout of charging station locations is presented, and the developed model is validated to work effectively. The model provides a theoretical way to optimize the layout of charging station locations and serves as a basis for layout planners and a reference for other researchers.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Demand Forecasting-Based Layout Planning of Electric Vehicle Charging Station Locations


    Beteiligte:
    Li, Min (Autor:in) / Wang, Wuhong (Autor:in) / Mu, Hongfei (Autor:in) / Jiang, Xiaobei (Autor:in) / Ranjitkar, Prakash (Autor:in) / Chen, Tao (Autor:in)


    Erscheinungsdatum :

    2017-07-12


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Demand Forecasting-Based Layout Planning of Electric Vehicle Charging Station Locations

    Li, Min / Wang, Wuhong / Mu, Hongfei et al. | British Library Conference Proceedings | 2018




    Optimization Model of Electric Vehicle Charging Station Layout

    Zhao, Jiayi / Huang, Linshuying / Lv, Xinli et al. | TIBKAT | 2020


    Electric vehicle charging demand forecasting using deep learning model

    Yi, Zhiyan / Liu, Xiaoyue Cathy / Wei, Ran et al. | Taylor & Francis Verlag | 2022