Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper the authors model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents.

• Record URL:
  • https://doi.org/10.1016/j.procs.2018.04.080
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050918304423
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2018 Guus Berkelmans et al. Published by Elsevier B.V. 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018).
• Authors:
  • Berkelmans, Guus
  • Berkelmans, Wouter
  • Piersma, Nanda
  • van der Mei, Rob
  • Dugundji, Elenna
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: References; Tables;
• Pagination: pp 549-556
• Serial:
  • Procedia Computer Science
  • Volume: 130
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Automobile travel; Demand; Electric vehicle charging; Infrastructure; Taxi services; Vehicle sharing
• Geographic Terms: Amsterdam (Netherlands)
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01676937
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 27 2018 3:36PM