The infor

Work

Individual and combined impacts are assessed based on the “Kaya Identity,” modified for this analysis in two key ways:(1) Populations of AVs and CVs are separated for clarity(2) This analysis uses liquid fuel demand as the output rather than CO2 to 

isolate this issue from the CO2 intensity of electricity or other fuels.

ൈ ݏ#ܸ݄݈݁݅ܿ݁ ൌ ݁݉ܽ݊݀ܦ	݈݁ݑܨ

ൈ ݏܸܣ%

ܶܯܸ

൅ ݕ݃ݎ݊݁ܧ ௏஺ݏ݅݀ݑݍ݅ܮ ൈ ܶܯܸ ௏஺ݕ݃ݎ݊݁ܧ ൈ ܸܣ

ൈ ݏܸܣ% െ 1

ܶܯܸ

ݕ݃ݎ݊݁ܧ ௏஼ݏ݅݀ݑݍ݅ܮ ൈ ܶܯܸ ௏஼ݏ݅݀ݑݍ݅ܮ ൈ ܸܥ

The top term represents fuel use by AVs and the bottom term by CVs. We refer to factors affecting VMT/vehicles as “use intensity” (UI); factors affecting Energy/VMT as “energy intensity” (EI); and factors affecting Liquids/Energy 

(e.g. electric vehicles use no liquid fuels) as “fuel intensity” (FI). This analysis uses 2030 as an example year for reference. This is predictive, but as a baseline for comparison. Each potential impact examined below is translated into one or more effects on the terms in the equations above. Where possible, we adapt estimates from other sources as they might apply to AVs. Effects are assumed to be independent for this analysis, so impacts are chain multiplied to combine. Each effect is described by:

Other Potent

NREL is a national laboratory of the U. 

and Renewable Energy, o

Autonomous Vehicles Have a Wide Range of Possible Ener

Austin Brown1, Brittany Repac2, Jeff Gonder11. National Renewable Energy Laboratory 2. University of Maryland, College Park

MethodPrivate Ownership (Low Number of AVs)

Shared Ownership (High Number of AVs)

Fuel Use Impact Ranges (Per Vehicle)

Synergies with Electric Vehicles

Introduction

Conclusion

Self-driving or “autonomous” vehicles (AVs) have leapt from science fiction into the forefront of transportation technology news. The technology is likely still years away from widespread commercial adoption, but the recent progress makes it worth considering the potential national impacts of widespread implementation. This poster makes an initial assessment of the energy impacts of AV adoption on a per-vehicle basis and on total personal vehicle fuel use. While AVs offer numerous potential advantages in energy use, there are significant factors that could decrease or even eliminate the energy benefits under some circumstances. This analysis attempts to describe, quantify, and combine many of the possible effects. The nature and magnitude of these effects remain highly uncertain. This set of effects is very unlikely to be exhaustive, but this analysis approach can serve as a base for future estimates.

Effect Approach Effect Estimate Estimate SourceA brief description of the possible effect

How this posterestimates the impact

Estimated % change to UI, EI, FI

References used in developing the estimate

These effects do not require strong system effects and could manifest themselves with low penetration cases where most AVs are owned by individuals. Here we assume 10% penetration.Effect Approach Effect Estimate Estimate Source(a) Platooning: close

following at high speed to reduce drag

Use estimates of overall savings potential from literature

-10% EI MIT technology review 

(2011); Ahn, Rakha, and Park (2013); RITA

(b) Efficient driving: smooth start stop, some stop elimination

Use estimates of ecodrivingpotential

-20% to -30% EI Gonder, Earlywine, and 

Sparks (2012)

(c) Efficient routing: traffic

avoidance and most efficient route selection

Example case from Buffalo, NY

-20% EI Sadek and Guo (2011)

(d) Travel by underserved populations: (youth, disabled, and elderly)

Estimate the additionalmiles if all people over 13 had the VMT of the highest demographic

+70% UI Author’s estimate based 

on NHTS data

Effect Approach Effect Estimate Estimate Source(e) Efficient driving: full stop elimination and trip smoothing

Use upper bound of efficiency improvement from smooth travel

-10 to -20% EI (additional to previous estimate)

Gonder, Earlywine, and Sparks (2012)

(f) Faster travel:possible due to safe highway operation

Estimate impact on fuel economy from aerodynamic drag at 100 MPH

+30% EI Author’s estimate based on 

ORNL 2013

(g) More travel: due to faster travel, reduced traffic, people may live further from destinations or travel more 

Assume the current time spent travelling remains the same (so miles increase with speed)

+50% UI Author’s estimate; Schaefer

et al. (2009)

(h) Lighter vehicles:Very few crashes could enable very light vehicles for many duty cycles

Assume weight could be reduced ~75% and each 10% reduction = 6% EI reduction

-45% EI Author’s estimate; Burns 

(2012)

(i) Less time looking for parking: from fewer vehicles and self parking

Assume it cuts the wasted fuel in half

-4% UI Author’s estimate; TTI Urban 

Mobility Report

(j) Higher occupancy: facilitated by IT, automated carpooling

Use the upper bound estimates for "dynamic ridesharing"

-12-20% UI Transportation Energy 

Futures (2013)

In this scenario, AV use is widespread enough to make private ownership less necessary, with users instead summoning a shared use vehicle for their immediate need. Widespread adoption without vehicle sharing is also possible, but is not considered here. The effects below become possible as penetrations increase so the majority of vehicles on the road are automated. Here we assume 90% penetration.

Effect Approach Effect Estimate Estimate Source

(k) Electrification:deployed vehicle could be matched to user trip need

Estimate the share of vehicle trips that could be met with a 40 mi range EV

-75% FI (as a -100% FI to 75% of vehicles)

Author’s estimate; NHTS;Krumm 2012

AVs have the potential to mindividuals. Most possible savings, but many effects increases in fuel use, depepossible impacts range fbenefits occur) to more tincreases are considereenergy impacts in AV depl

Bullis. (2012). "How Vehicle Automation Will Cut Fuel Consumption." MIT Technology Review. Ahn, Rakha, and Park. (2012). “ECO-Drive Application: Algorithmic Development and Preliminary Testing.” 92nd Transportation Research Boa(2012). Analyzing Vehicle Fuel Saving Opportunities Through Intelligent Driver Feedback. Sadek and Suo. (2011). "An Evaluation of Likely Environmental Benefits of a Time-dependent Green Routing System in the Greater Buffalo-Niagara 

Vehicles: Statistics from the 2009 National Household Travel Survey.” Thomas et al. (2013). Predicting Light-Duty Vehicle Fuel Economy as a Function of Highway Speed. ORNL: SAE technical paper. Schaefer et al. (2009). “Transportatio

A&M Transportation Institute (TTI). (2012) TTI’s 2012 Urban Mobility Report. Porter et al. (2013). Effects of Travel Reduction and Efficient Driving on Transportation: Energy Use and Greenhouse Gas Emissions. Transportation Energy F

Baseline assumptions: 262 million vehicles, 47.2 MPG reference vehicle, 12700 miles / vehicle / year (from Annual Energy Outlook 2013).

References and 

AVs would have many pot• Embodied energy bene

road (Burns 2012), so in vehicles, leading to low

• Economic benefits: with 

shared vehicle scenario

• Social benefit of transpo

use but provides valuab

• Land use benefit: with f

repurpose land from pa

• Safety benefits: these w

fewer vehicle replaceme

• Interaction with mass tr

and lower labor costs fo

private ownership model.

shared ownership model.

synergies with electric vehicles.

Total Scenari

Private O

Low Penetrat

ScenarioNumber

Name 

1 Private Ownership, F2 Private Ownership, F3 Private Ownership, C4 Shared Vehicles, Fue5 All Identified Potentia6 Vehicle Electrification 7 All Identified Potentia8 All Effects 

Shared AVs may be more amenable to electrification since a vehicle can be dispatched to meet a user’s specific need, only serving trips within range.

-50%

+70%

-100%

+100%

Use Intensity Energy Intensity Fuel Intensity

-23% -56%

+50% +35%

-100%

+100%

-75%

-100%

+100%

Per-vehicle potential effects range from large increases in fuel use to large savings depending on the scale and interaction of the various factors.

-5% 

8%

-100%

-50%

0%

50%

100%150%200%250%

12

Scenario change in total 

light-duty vehicle fuel demand