Integrating plug-in electric vehicles (PEVs) into household fleets- factors influencing miles traveled by PEV owners in California. (January 2022) - Chakraborty, Debapriya; Hardman, Scott; Tal, Gil
Highlights: Similar to conventional fossil fuel vehicles, PEV VMT is correlated to traditional factors like population density, built environment, attitudes towards PEV technology and lifestyle preferences. Specific to PEVs, electric driving range, and access to infrastructure, have a major influence on PEV VMT. While lower electricity price at home may lead to higher share of PEV in total household VMT, we do not identify the presence of "rebound effect". PEVs drive a similar amount as conventional vehicles, not more (due to rebound) or less (due to range anxiety) as some have suggested. Abstract: Household vehicle miles traveled (VMT) has traditionally been studied in the context of gasoline vehicles. In this study, we analyze VMT of Plug-in Electric Vehicles (PEVs) to understand PEV use and the factors that influence their use in a household. We use data from a unique repeat survey of PEV owners in California who were originally surveyed when they first bought their vehicle. Having two survey responses per household allows us to obtain more reliable VMT data and analyze the change in VMT over the vehicle ownership period. The results show that PEV VMT is correlated with traditional factors like population density, built environment, attitudes towards technology, and lifestyle preferences. Specific to PEVs, electric driving range and access to infrastructure, have a major influence on PEV VMT. Moreover, while lower electricity price at home may lead to a higher share of PEV VMT in total household VMT, we do not identify the presence of "rebound effect". Overall, we observe that factors influencing PEV VMT are like those observed for conventional gasoline vehicles. We find that PEVs drive a similar amount as conventional vehicles, not less (due to range anxiety) as some have suggested. The results have implications for emissions impact assessments and travel demand models that depend on assumptions of average annual VMT for policymaking.