Development of a DC Fast Charging Station Model for Use with EV Infrastructure Projection Tool

Andrew Meintz, Emin Ucer, Mithat Kisacikoglu, Fatih Erden, Clement Rames

Research output: Contribution to conferencePaperpeer-review

12 Scopus Citations

Abstract

The deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition, and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations' loading, utilization, and customer service quality (i.e. queuing time, charging duration, and queue length). This study aims to analyze these aspects using one million vehicle-days of travel data within the Columbus, OH, region. Monte Carlo analysis is carried out in three types of areas - urban, suburban, and rural- to quantify the effect of uncertain parameters on DCFC station loading and service quality.

Original languageAmerican English
Pages934-938
Number of pages5
DOIs
StatePublished - 28 Aug 2018
Event2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018 - Long Beach, United States
Duration: 13 Jun 201815 Jun 2018

Conference

Conference2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
Country/TerritoryUnited States
CityLong Beach
Period13/06/1815/06/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/CP-5400-71245

Keywords

  • batteries
  • charging stations
  • data models
  • load modeling
  • power demand
  • state of charge
  • tools

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