Autonomous vehicles are one of the main sources of uncertainty in the future of U.S. transportation energy consumption, as autonomous vehicle technology has the potential to change travel behavior, vehicle design, energy efficiency, and vehicle ownership. Analysis in EIA’s Annual Energy Outlook 2018 (AEO2018) shows that the widespread adoption of autonomous vehicles could increase overall light-duty vehicle travel and, depending on how those vehicles are powered, lead to slightly higher transportation energy consumption.

On-road vehicles, including light-duty vehicles, buses, and commercial and freight trucks, are significant consumers of energy in the United States, accounting for 31% of all delivered end-use energy. Light-duty vehicles alone account for 21% of total delivered end-use energy consumption. EIA projects that light-duty vehicle travel will continue increasing in the future. By 2050, light-duty vehicle miles traveled will reach 3.3 trillion miles, or 18% higher than the 2017 level.

In two AEO2018 sensitivity cases that assume more widespread use of autonomous vehicles—and that these vehicles are driven more miles per year than non-autonomous vehicles—than in the Reference case, overall light-duty vehicle travel demand increases 14% higher than Reference case levels by 2050, reaching 3.8 trillion miles in that year. One case assumes the increasing adoption of autonomous battery electric vehicles; another case assumes the increasing adoption of autonomous hybrid electric vehicles. Both cases assume that autonomous vehicles as a share of overall light-duty vehicle sales increase from 1% percent in the Reference case to 31% in the sensitivity cases in 2050.

In the AEO2018 Reference case, autonomous vehicles are powered by conventional gasoline internal combustion engines. Despite the relative fuel efficiency of battery electric and hybrid electric vehicles compared with conventional gasoline internal combustion engines, more energy is consumed in both sensitivity cases (up to 4% more) compared with Reference case levels in 2050 because of increased light-duty vehicle travel. In all three cases, however, conventional gasoline engines remain the most common technology powering light-duty vehicles.

light-duty vehicle sales by fuel type in three scenarios, as explained in the article text


Future transportation energy demand in the Reference and both sensitivity cases is still lower than in 2017, largely because of Corporate Average Fuel Economy and greenhouse gas emissions standards on light-duty vehicle energy consumption. In the two sensitivity cases with greater autonomous vehicle adoption, transportation energy demand is slightly higher than in the Reference case, as the improved fuel economy associated with battery electric vehicles and hybrid electric vehicles only partially offsets the increase in travel demand.

In the sensitivity case with more hybrid electric vehicles, gasoline consumption is slightly higher than in the Reference case. In the case with more battery electric vehicles, electricity consumption is slightly higher than in the Reference case. In both cases, diesel consumption is virtually unchanged.

transportation motor gasoline, diesel, and electricity energy consumption, as explained in the article text


In both sensitivity cases, fuel use in public transit modes is affected by assumptions about how they could interact with autonomous vehicles. Large fleet long-haul commercial trucks are assumed to start using automation technology to improve fuel efficiency through platooning, where groups of vehicles travel in a tight formation to reduce aerodynamic drag. However, the energy consumption effects of these changes for commercial trucks or other modes such as mass transit are relatively small compared with the consumption changes in light-duty vehicles.

The full Issues in Focus article Autonomous Vehicles: Uncertainties and Energy Implications provides additional discussion of definitions of autonomous vehicles, potential benefits and obstacles, uncertainties related to energy consumption, and scenario assumptions and results.

Principal contributor: Nicholas Chase

Source link

NO COMMENTS

LEAVE A REPLY