Charging Uncertainty: Real-Time Charging Data and Electric Vehicle Adoption

46 Pages Posted: 14 Jan 2025 Last revised: 7 Feb 2025

See all articles by Omar Asensio

Omar Asensio

Georgia Tech School of Public Policy and Institute for Data Engineering & Science (IDEaS)

Elaine Buckberg

Harvard University

Cassandra Cole

Harvard University

Luke Heeney

Massachusetts Institute of Technology (MIT)

Christopher R. Knittel

Massachusetts Institute of Technology (MIT) - Center for Energy and Environmental Policy Research (CEEPR); National Bureau of Economic Research (NBER)

James H. Stock

Harvard University - Department of Economics; National Bureau of Economic Research (NBER); Harvard University - Harvard Kennedy School (HKS)

Date Written: January 2025

Abstract

Charging infrastructure is critical to electric vehicle (EV) adoption, but for chargers to be most useful, EV drivers need to know in real time where they are and whether they are working and available. We investigate the availability of real-time data from DC fast chargers on six major US Interstates and model the impacts of expanding access to real-time data to all DC fast chargers near highways. On average, between March and August 2024, 32.9% of DC fast charging stations adjacent to those six Interstates provided their real-time status on PlugShare, a major charge-finding app, with gaps of up to 1,308 miles without real-time data. Further, we survey potential car buyers and EV owners and find low credibility of currently-available real-time data. We incorporate this data into a two-sided model of consumer vehicle choice and charging station build-out adapted from Cole et al. (2023). If universal real-time data is accompanied by improved charger uptime and driver confidence in the accuracy of the real-time data, we predict that the EV share of new vehicle sales would grow by 8.0 percentage points in 2030, expanding the EV fleet by 13.2%, and reducing 2030 carbon emissions by 22.5 mmt, versus baseline projections for 2030.

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Suggested Citation

Asensio, Omar and Buckberg, Elaine and Cole, Cassandra and Heeney, Luke and Knittel, Christopher R. and Stock, James H., Charging Uncertainty: Real-Time Charging Data and Electric Vehicle Adoption (January 2025). NBER Working Paper No. w33342, Available at SSRN: https://ssrn.com/abstract=5094966

Omar Asensio (Contact Author)

Georgia Tech School of Public Policy and Institute for Data Engineering & Science (IDEaS) ( email )

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Elaine Buckberg

Harvard University ( email )

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Cassandra Cole

Harvard University ( email )

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Luke Heeney

Massachusetts Institute of Technology (MIT) ( email )

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Christopher R. Knittel

Massachusetts Institute of Technology (MIT) - Center for Energy and Environmental Policy Research (CEEPR) ( email )

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National Bureau of Economic Research (NBER)

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James H. Stock

Harvard University - Department of Economics ( email )

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National Bureau of Economic Research (NBER)

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Harvard University - Harvard Kennedy School (HKS) ( email )

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