Data-Driven Method for Electric Vehicle Charging Demand Analysis: Case Study in Virginia: Article No. 103994

Research output: Contribution to journalArticlepeer-review

Abstract

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.
Original languageAmerican English
Number of pages19
JournalTransportation Research Part D: Transport and Environment
Volume125
DOIs
StatePublished - 2023

NREL Publication Number

  • NREL/JA-2C00-88144

Keywords

  • connected vehicle data
  • electric vehicle charging demand
  • land use data
  • trip chaining

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