Anticipating Electric Vehicle Adoption Patterns and Emissions Impacts

Photo Credit: Fairfax News

When you purchase your next vehicle, will you consider buying an electric vehicle (EV)? If you already own an EV, are you wondering about the future of EV ownership and how increased usage might impact the number and placement of charging stations? Is increased EV use actually having a positive environmental impact?

Researchers at the University of Virginia (UVA) and Old Dominion University (ODU) are taking these issues seriously. Funding from MATS UTC is enabling them to model anticipated EV adoption patterns, giving them the ability to predict which households in which neighborhoods are most likely to own such vehicles. Combined with influence factors such as technology familiarity, vehicle availability, charging infrastructure provision and demographics, this predictive model could play a vital role in informing public policy related to power-grid planning, transportation investments and air quality strategies.

Taking a two-pronged research approach, the researchers are assessing both revealed preferences (RP) and stated preferences (SP) to understand how various factors influence vehicle choice. T. Donna Chen, PE, PhD, department of civil and environmental engineering at UVA, along with PhD student, Wenjian Jia, and Rajesh Paleti, PhD, department of civil and environmental engineering at ODU, are analyzing vehicle registration data (2008 to 2016) from the Virginia Department of Motor Vehicles (DMV) to develop a snapshot of RP related to actual adoption and use among regional EV owners. In the spring of 2018, survey data will be collected to determine SP toward vehicle traits, land use and EV infrastructure. A predictive spatial model is being developed based on this individual and geographic level data to better understand the impact of various regional factors and household demographics on EV adoption.

The research team is working with Virginia Clean Cities, a non-profit organization focused on reducing reliance on gas-powered vehicles, to reach out to EV owners and stakeholders. “We’re directly engaging with consumers and industry, developing an applications-based approach to help transportation planners, utility providers and public policy professionals make informed planning decisions based on when and where new EV households will emerge,” explained Chen. “These results may also lead to better insights about air quality impacts and the effect of EV adoption trends on the carbon footprint in Virginia.”

These findings will help to create a more accurate profile of EV adopters in Virginia. Ultimately, the modeling capabilities could provide a decision framework to determine the most effective placement of charging stations as well as the effects of various policies, such as rebates or usage fees, on actual EV adoption and use.

For more information, contact Dr. Chen at or 434-924-6224.