Modeling of Charging Station Choice among EV Drivers Based on Prospect Theory

This study presents a prospect theory model, focusing on electric vehicle (EV) drivers’ choice from a set of charging stations. The research consists of three parts including driver classification, model construction and model validation. Based on the state of charging (SOC) and charging time, the EV drivers are divided into three categories and these three types have different risk propensity which affects drivers’ choices. The prospect value is calculated according to the drivers’ categories, the detour distance, charging cost, and time cost. The proposed model is validated by a small scale case study in Shanghai. A useful method to determine the reasonable alternative charging stations is also provided to avoid time-consuming searching among all stations in Shanghai. The parameters are also calibrated according to the large scale case study.

• Record URL:
  • https://doi.org/10.1061/9780784482292.056
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Wang, Yunshan
  • Jiang, Tianqi
  • Lu, Yangzi
  • Xu, Zhaoqi
  • Xia, Yan
  • Liu, Zhiyuan
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 615-627
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Calibration; Case studies; Choice models; Costs; Distance; Drivers; Electric vehicle charging; Validation
• Geographic Terms: Shanghai (China)
• Subject Areas: Energy; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01719553
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Oct 21 2019 9:45AM