Electric vehicle market potential and associated energy and emissions reduction benefits

Ziyi Dai, Haobing Liu, Michael O. Rodgers, Randall Guensler

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this paper, a methodological framework is proposed to assess the potential of electric vehicle (EV) penetration and corresponding reduction in energy use and emissions in Georgia, U.S., using 2017 National Household Travel Survey data. A two-phase, data-driven model assesses the potential for household purchases of EVs and the assignment of EVs to household trips. Households sharing the highest similarities are selected as candidates for EV purchases, with household trips identified as EV-amenable or not. Potential EV-purchasing families were also matched to specific EV makes and models. Energy use, greenhouse gas emissions, and criteria air pollutants were analyzed and compared for all original trips and for those trips that shifted to EVs. By comparing against two traditional averaging methods, the framework demonstrates an advancement that helps to avoid the overestimation of EV benefits. By integrating household-level demographics and trip-level attributes from open-source travel survey data in EV adoption and trip assignment, this paper demonstrates the benefits associated with EV adoption (a potential 45% reduction in energy consumption and 30% reduction in greenhouse gas emissions), moreover, the proposed methodology could serve as an innovative framework that is scalable and transferable to predict the future market penetration and actual on-road EV activities under various contexts.

Original languageEnglish
Article number119295
JournalApplied Energy
Volume322
DOIs
StatePublished - Sep 15 2022
Externally publishedYes

Funding

This work was sponsored by a grant from the National Center for Sustainable Transportation .

Keywords

  • Adoption and impact modeling
  • Electric vehicles
  • Energy use and emissions
  • National household travel survey
  • Random forest ensemble
  • Similarity measure

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