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Autonomous Vehicles: Uncertainties and Energy Implications For Fourth International Transport Energy Modeling (iTEM4) workshop October 30, 2018 | Laxenburg, Austria By John Maples, Team Lead Nicholas Chase, Lead Economist U.S. Energy

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www.eia.gov

U.S. Energy Information Administration

Independent Statistics & Analysis

Autonomous Vehicles: Uncertainties and Energy Implications

For Fourth International Transport Energy Modeling (iTEM4) workshop October 30, 2018 | Laxenburg, Austria By John Maples, Team Lead Nicholas Chase, Lead Economist

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Overview

  • Background
  • AEO2018 Issues in Focus
  • Ongoing work

– Levels 1-3 automated technology adoption – Multiyear effort to model key energy effects of automated vehicles – Geographic population density and travel patterns

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 2

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Background

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 3

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Definition of vehicle automation

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 Source: U.S. Department of Transportation, Automated Driving Systems 2.0, A Vision for Safety 4

  • Operational and safety-critical control functions occur without driver input
  • Connected and automated vehicles
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Potential benefits underlie interest but there are also key uncertainties and obstacles

Benefits

  • Road safety
  • Increased system efficiency

– Route harmonization – Reduced congestion

  • Increased mobility for

underserved population

  • Less time driving

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 5

Obstacles

  • Consumer acceptance
  • Technology cost and function
  • Cybersecurity
  • Legal framework
  • Infrastructure
  • Policy
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10 20 30 40 50 high energy efficiency with less vehicle miles traveled light-duty vehicles (2017) low energy efficiency with more vehicle miles traveled 2017 U.S. delivered energy consumption quadrillion Btu

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Range of potential effects of autonomous vehicles on light-duty vehicle energy consumption

Source: 2017: EIA, AEO2018 Reference case, extrapolation based on upper and lower limits from Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles (Stephens et al) 6

+200%

  • 60%

15.3 quadrillion Btu (8.3 million b/d oil equivalent) 45.9 quadrillion Btu (24.9 million b/d oil equivalent) 6.1 quadrillion Btu (3.3 million b/d oil equivalent)

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  • 75% -50% -25%

0% 25% 50% 75% 100% 125% 150% 175% 200%

Changes in light-duty vehicle miles traveled % range

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

There is uncertainty about how highly automated vehicles could affect future transportation energy demand

Sources: Help or Hindrance? The Travel, Energy, and Carbon Impacts of Highly Automated Vehicles (Wadud et al); Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles (Stephens et al) 7

more energy less energy ease cost of driving underserved population empty miles mode switching ridesharing MaaS parking

  • 75% -50% -25%

0% 25% 50% 75% 100% 125% 150% 175% 200%

more energy less energy eco-driving V2I de-emphasized performance faster travel traffic flow + drive profile increased feature content collision avoidance less congestion platooning vehicle resizing

Changes in light-duty vehicle fuel efficiency % range

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Additional ways vehicle automation technology could affect transportation energy consumption

  • Alternative fuels and energy efficient powertrains
  • Commercial trucks
  • Mass transit

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 8

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AEO2018 Issues in Focus— Autonomous Vehicles: Uncertainties and Energy Implications

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 9

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Description of scenarios

  • Reference case

– Autonomous vehicles enter fleet light-duty vehicles

  • 1% of new sales by 2050

– Autonomous vehicles used more intensively

  • 65,000 miles/year and scrapped more quickly

– Autonomous vehicle fuel type

  • 100% conventional gasoline internal combustion engine

– Autonomous vehicles affect mass transit

  • Increases use of commuter rail
  • Decreases use of transit bus and transit rail

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 10

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Description of scenarios–two scenarios examine energy implications from more widespread use of autonomous vehicles

  • Identical assumptions

– Autonomous vehicles enter household and fleet light-duty vehicles

  • 31% of new sales by 2050

– Autonomous vehicles used more intensively

  • 65,000 miles/year (fleet) ; +10% miles/year (household) on average

– Autonomous vehicles affect mass transit modes

  • Increases use of commuter rail
  • Decreases use of transit rail
  • Decreases use of transit bus until mid-2030s, thereafter, increases transit bus use from

automation technology – Automation technology included on long-haul fleet commercial trucks enables platooning

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 11

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Description of scenarios–two scenarios examine energy implications from more widespread use of autonomous vehicles

  • Autonomous Battery Electric Vehicle case

– Increasing share of autonomous vehicles are battery electric through 2050

  • 96% of fleet and 82% of household autonomous vehicles by 2050
  • Autonomous Hybrid Electric Vehicle case

– Increasing share of autonomous vehicles are hybrid electric through 2050

  • 96% of fleet and 71% of household autonomous vehicles by 2050

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 12

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2 4 6 8 10 12 14 16 18 20 2020 2030 2040 2050 2020 2030 2040 2050 2020 2030 2040 2050 Conventional gasoline Battery electric Hybrid electric Other U.S. light-duty vehicle sales million

Light-duty vehicle sales by fuel type across scenarios

Source: EIA, AEO2018 Reference case, Autonomous Battery Electric Vehicle case, Autonomous Hybrid Electric Vehicle case 13 John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Reference case Autonomous Hybrid Electric Vehicle case Autonomous Battery Electric Vehicle case

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2,000 2,200 2,400 2,600 2,800 3,000 3,200 3,400 3,600 3,800 4,000 2010 2015 2020 2025 2030 2035 2040 2045 2050 U.S. light-duty vehicle miles traveled billion

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Light-duty vehicle miles traveled 14% above Reference case in 2050 and 35% higher in 2050 than in 2017

Source: EIA, AEO2018 Reference case, Autonomous Battery Electric Vehicle case, Autonomous Hybrid Electric Vehicle case 14

≈ history projection 2017 Reference case Autonomous Battery Electric Vehicle case Autonomous Hybrid Electric Vehicle case

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22 24 26 28 2010 2015 2020 2025 2030 2035 2040 2045 2050 U.S. transportation energy consumption quadrillion Btu

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Transportation energy consumption higher in both cases compared to Reference case but still lower than 2017

Source: EIA, AEO2018 Reference case, Autonomous Battery Electric Vehicle case, Autonomous Hybrid Electric Vehicle case 15

≈ history projection 2017 Reference case Autonomous Battery Electric Vehicle case Autonomous Hybrid Electric Vehicle case

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2 4 6 8 10 12 14 16 18 2010 2020 2030 2040 2050 Transportation energy consumption by fuel quadrillion Btu

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Transportation fuel consumption differs between cases because of changes in light-duty vehicle fuel type

Source: EIA, AEO2018 Reference case, Autonomous Battery Electric Vehicle case, Autonomous Hybrid Electric Vehicle case 16

motor gasoline 2 4 6 8 10 12 14 16 18 2010 2020 2030 2040 2050 diesel Reference case Autonomous Hybrid Electric Vehicle case Autonomous Battery Electric Vehicle case 2 4 6 8 10 12 14 16 18 2010 2020 2030 2040 2050 electricity

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Ongoing work

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 17

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18

The BRAIN

PMT VMT

Availability

  • f transit

Cost & utility of vehicle

  • wnership

Availability

  • f ride

hailing Empty miles Underserved population Population density Cost ride hailing Cost transit Deliveries

Urban v. suburban

  • v. rural

ROI

Tech cost curve Fuel economy Scrappage rates

Traditional

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Recent modeling

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Recent modeling focus: adding levels of highly automated vehicles—

  • Levels of vehicle automation (introduction year, cost, weight, fuel economy,

etc.):

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 19

automation level description Level 1

driver assistance technology

Level 2

partial automation technology

Level 3

conditional automation technology

Level 4a

low speed (<35 mpg) operation in limited geofenced areas such as urban centers

Level 4b

full speed operation in limited geofenced areas such as limited access highways

Level 5

fully autonomous vehicle that can operate on all roads and all speeds

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Recent modeling focus–and the economics of ride-hailing fleet adoption

  • Separates taxi fleet (taxi and future Transport Network Companies) with

unique VMT and scrappage curves

  • Economics of adoption:

– Return on Investment (ROI) as net present value (NPV) of fare revenue minus operating cost (driver, revenue miles, data costs, etc.) – Logit function adoption with (dis)utilities related to new technology and operational domain parameters – Technology cost:

  • LiDAR system (low-resolution and high-resolution) as experience function with time-

based R&D

  • HAV system as time-based R&D function

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 20

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Example of highly automated vehicle cost and sales into ride-hailing fleet

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 21

0% 10% 20% 30% 40% 50% 60% 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049

share of ride-hail fleet sales

L4a L4b L5 $0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 2015 2017 2019 2021 2023 2025 2027 2029 2031 2033 2035 2037 2039 2041 2043 2045 2047 2049

vehicle cost

Series1 Series2 Series3 Series4

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22

The BRAIN

PMT VMT

Availability

  • f transit

Cost & utility of vehicle

  • wnership

Availability

  • f ride

hailing Empty miles Underserved population Population density Cost ride hailing Cost transit Deliveries

Urban v. suburban

  • v. rural

ROI

Tech cost curve Fuel economy Scrappage rates

Traditional

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Recent modeling Current research

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0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 1 2 3 4 5 6 7 8 9 rural suburban urban

Census Division

Share of U.S. population by geographic density %

U.S. population by geographic density and Census Division

Source: U.S. Census Bureau, American Community Survey (ACS) 2015 23 John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

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Thank you

John Maples | phone: 202-586-1757 | email: john.maples@eia.gov Nicholas Chase | phone: 202-586-1879 | email: nicholas.chase@eia.gov Autonomous Vehicles: Uncertainties and Energy Implications | https://www.eia.gov/outlooks/aeo/section_issues.php#av U.S. Energy Information Administration home page | www.eia.gov Annual Energy Outlook | www.eia.gov/outlooks/aeo

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 24

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Supplemental slides

John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018 25

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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

  • ver 900 CBSAs (largest  smallest)

Share of U.S. population within CBSAs by geographic density %

U.S. population living in Core Based Statistical Areas (CBSAs) by geographic density

Source: U.S. Census Bureau, American Community Survey (ACS) 2015 26 John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

total population suburban urban rural

Top 2 cities (10 million +) Top 9 cities (5 million +) Top 52 cities (1 million +) Top 105 cities (500 thousand +) Top 188 cities (250 thousand +) Top 412 cities (100 thousand +)

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John Maples and Nicholas Chase, Laxenburg, Austria, October 30, 2018

Core Based Statistical Areas (CBSAs) define commuter regions

Source: U.S. Census 27