@article{0e5ea2b7735d4d25a95120c06f7e843b,
title = "Multistage and dynamic layout optimization for electric vehicle charging stations based on the behavior analysis of travelers",
abstract = "Electric vehicles (EV) are growing fast in recent years with the widespread concern about carbon neutrality. The development of charging infrastructures needs to be in phase with EV both in terms of quantity and charging time to decrease the range anxiety of EV users and resource waste. This paper proposed a multistage and dynamic layout optimization model based on mixed integer linear programming (MILP) for EV charging stations (CSs) to minimize the total social costs (TSC) consisting of the detour cost of EV users and the construction, relocation, and operating cost of CSs. The charging satisfaction coefficient and M/M/S/K model of queuing theory has been introduced to determine the desirable charging supply. The spatial-temporal distribution of charging demand was modeled based on the behavior analysis of travelers and over the discrete-time intervals for a day. Comparison studies based on the Sioux Falls network reveal that TSC with a multistage optimization strategy will drop 8.79% from that with a one-time optimization strategy. Charging service quality, relocation cost, and road network scales have a significant impact on the optimization results according to the sensitivity analysis.",
keywords = "Behavior analysis, Charging station, Electric vehicle, MILP, Multistage layout",
author = "Feng Chen and Minling Feng and Bing Han and Shaofeng Lu",
note = "Funding Information: Funding: The author(s) disclosed the receipt of the following financial support for the research, authorship, and/or publication of this article: The work described in this paper has received funding from the Featured Creative Project of Higher Education Institution of Guangdong Province, China (No. 2021KTSCX001), in part by the Fundamental Research Funds for the Central Universities (No. 2020ZYGXZR087), and the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2021K008), Beijing Jiaotong University. Funding Information: The author(s) disclosed the receipt of the following financial support for the research, authorship, and/or publication of this article: The work described in this paper has received funding from the Featured Creative Project of Higher Education Institution of Guangdong Province, China (No. 2021KTSCX001), in part by the Fundamental Research Funds for the Central Universities (No. 2020ZYGXZR087), and the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2021K008), Beijing Jiaotong University.Acknowledgments: We would like to thank Fei Xue from Xi?an Jiaotong-Liverpool University for his generous support and useful discussions during the preparation of this paper. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = dec,
doi = "10.3390/wevj12040243",
language = "English",
volume = "12",
journal = "World Electric Vehicle Journal",
issn = "2032-6653",
number = "4",
}