摘要
This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.
作者
张轶伦
徐思坤
徐捷
曾学奇
李铮
谢驰
ZHANG Yilun;XU Sikun;XU Jie;ZENG Xueqi;LI Zheng;XIE Chi(Department of Industrial Engineering and Management,Shanghai Jiao Tong University,Shanghai 200240,China;Olin Business School,Washington University in St.Louis,St.Louis,MO 63130,USA;Urban Mobility Institute,Tongji University,Shanghai 201804,China;Antai College of Economics and Management,Shanghai Jiao Tong University,Shanghai 200030,China;College of Transportation Engineering,Tongji University,Shanghai 201804,China)
基金
the National Natural Science Founda-tion of China(Nos.72171175,and 72021102)。
