Highlights We address the optimal location of multiple types of BEV charging facilities, including dynamic wireless charging facilities and different levels of plug-in charging stations.A tri-level programming is developed to model the presented problem.An efficient solution algorithm is proposed to solve the model, wherein the formulated model is first treated as a black-box optimization, and then solved by an efficient surface response approximation model based solution algorithm.

    Abstract To reduce greenhouse gas emissions in transportation sector, battery electric vehicle (BEV) is a better choice towards the ultimate goal of zero-emission. However, the shortened range, extended recharging time and insufficient charging facilities hinder the wide adoption of BEV. Recently, a wireless power transfer technology, which can provide dynamic recharging when vehicles are moving on roadway, has the potential to solve these problems. The dynamic recharging facilities, if widely applied on road network, can allow travelers to drive in unlimited range without stopping to recharge. This paper aims to study the complex charging facilities location problem, assuming the wireless charging is technologically mature and a new type of wireless recharging BEV is available to be selected by consumers in the future other than the traditional BEV requiring fixed and static charging stations. The objective is to assist the government planners on optimally locating multiple types of BEV recharging facilities to satisfy the need of different BEV types within a given budget to minimize the public social cost. Road users’ ownership choice among multiple types BEV and BEV drivers’ routing choice behavior are both explicitly considered. A tri-level programming is then developed to model the presented problem. The formulated model is first treated as a black-box optimization, and then solved by an efficient surface response approximation model based solution algorithm.


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    Title :

    Locating multiple types of charging facilities for battery electric vehicles


    Contributors:


    Publication date :

    2017-01-13


    Size :

    26 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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