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Assessing Energy Impacts of Connected and Automated Vehicles at the U.S. National Level—Preliminary Bounds and Proposed Methods

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Road Vehicle Automation 5

Abstract

Connected and automated vehicles (CAVs) can have tremendous impacts on transportation energy use. Using published literature to establish bounds for factors impacting vehicle demand and vehicle efficiency, we find that CAVs can potentially lead to a threefold increase or decrease in light-duty vehicle energy consumption in the United States. Much of this uncertainty is due to possible changes in travel patterns (in vehicle miles traveled) or fuel efficiency (in gallons per mile), as well as future adoption levels and patterns of use. This chapter details the factors which go into these estimates, and presents a methodological approach for refining this wide range of estimated fuel consumption.

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Notes

  1. 1.

    Ridesharing refers to a net increase in vehicle occupancy resulting from two or more people riding together in a vehicle during some or all of their travel.

  2. 2.

    Inputs regarding powertrain adoption projections stem from EIA’s AEO and NREL’s Automotive Deployment Options Projection Tool (ADOPT) [10].

References

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Acknowledgements

This report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors acknowledge Eric Rask of Argonne National Laboratory for leading the Connected and Automated Vehicle Pillar of the SMART Mobility Laboratory Consortium. Rachael Nealer and David Anderson of the DOE Office of Energy Efficiency and Renewable Energy (EERE) played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance.

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Correspondence to Thomas S. Stephens .

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Stephens, T.S. et al. (2019). Assessing Energy Impacts of Connected and Automated Vehicles at the U.S. National Level—Preliminary Bounds and Proposed Methods. In: Meyer, G., Beiker, S. (eds) Road Vehicle Automation 5. Lecture Notes in Mobility. Springer, Cham. https://doi.org/10.1007/978-3-319-94896-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-94896-6_10

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