Electric vehicle market potential and associated energy and emissions reduction benefits. (15th September 2022)

• Record Type: Journal Article
• Authors: Dai, Ziyi; Liu, Haobing; Rodgers, Michael O.; Guensler, Randall
• Is Part Of: Applied energy. Volume 322(2022)
• Publication Date: 2022-09-15
• Subjects: Electric vehicles -- Similarity measure -- Random forest ensemble -- Energy use and emissions -- National household travel survey -- Adoption and impact modeling; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119295
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Proposed a two-phase data-driven modeling framework for EV purchase and adoption prediction. Combined the modeling of household potential for adopting EVs with the assignment of EV use to specific trips in the assessment of energy use and emissions before-and-after EV adoption. Demonstrated a network level of 40% reduction in energy consumption and 25% reduction in greenhouse gas emissions under 5% and 10% EV market penetration scenarios. Compared with two averaging methods for avoidance of overestimation of EV impacts and benefits. 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.