Highlights Formulating an optimization model for selecting public charging stations. Maximizing electrified vehicle-miles-traveled for potential environmental benefits. Modeling public charging demand by using large-scale vehicle trajectory data. Studying public charging infrastructure planning in Beijing as a case study. Providing suggestions in model formulation and execution to handle large-scale data.

    Abstract We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quickly regardless of charging speed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Optimal locations of electric public charging stations using real world vehicle travel patterns


    Contributors:
    Shahraki, Narges (author) / Cai, Hua (author) / Turkay, Metin (author) / Xu, Ming (author)


    Publication date :

    2015-01-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English