Research Objectives Preliminary Findings

Methodology 

Future Work

Important modeling inputs:

• Modeling period: 2018-2050

• Existing vehicle stock & new sales of different powertrains, including CACC 

capabilities (e.g., AEO projections, ADOPT Scenarios, Shladover & Greenblatt 

white paper scenarios)

• CACC impacts on vehicles’ fuel economy across speed bins (e.g., based on 

LBNL Aimsun micro-simulation analysis)

• National-level impacts of CACC on VMT across speed bins (e.g., LBNL microsimulation) and due to perceived changes in vehicle travel time and induced 

travel demand (e.g., ANL/UIC agent based simulations)

Research Questions

• What is the national-level energy impact 

of adopting connected and automated 

vehicles and technologies (e.g., 

Cooperative Adaptive Cruise Control 

examined here, eco-signal 

implementation, automated mobility 

districts applications)?

• How do different levels of CACC adoption 

affect on-road fuel economy for different 

vehicle powertrains?

• What changes in vehicle miles traveled 

distribution are induced by CACC 

adoption and what is the potentially 

induced change in demand, primarily on 

US freeways and highways?

Source: Stephens et al. (2016).

Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of 

Connected and Automated Vehicles, http://www.nrel.gov/docs/fy17osti/67216.pdf

National-Level Energy Impacts of 

Cooperative Adaptive Cruise Control (CACC) Systems

Eleftheria (Ria) Kontou Ph.D., Jacob Holden, Matteo Muratori Ph.D., Jeffrey Gonder

Transportation and Hydrogen Systems Center, National Renewable Energy Laboratory (NREL) 

Contact: ria.kontou@nrel.gov

This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract 

No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. The views expressed in the article do 

not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

This poster 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 Vehicles Pillar of 

the SMART Mobility Laboratory Consortium. The authors would particularly like to thank David Anderson and Michael Berube with DOE’s Office of Energy Efficiency and Renewable Energy (EERE) for helping to establish the 

project concept and advance its implementation, and for providing ongoing guidance and support.

The information contained in this poster is 

subject to a government license.

Automated Vehicles Symposium

San Francisco, California

July 9-12, 2018

Modeling Assumptions & Data Insights

• Insights and data from micro-simulation modeling of CACC vehicle use in a freeway 

stretch in Sacramento CA, conducted by Lawrence Berkeley National Lab (LBNL)

• Induced demand assumptions, using preliminary results of agent-based modeling 

simulations conducted by Argonne National Lab (ANL)

The methodology proposed accounts for vehicle stock evolution, fuel consumption 

changes due to CACC adoption for different vehicle powertrains, and vehicle 

miles traveled (VMT) distribution changes as well as impacts of induced demand

Data Inputs

External collaborators: Tom Stephens (ANL), Hao Liu, Xiao-Yun 

Lu, Steven Shladover (LBNL), Paul Leiby, Zhenhong Lin (ORNL), 

Ramin Shabanpour, Abolfazl (Kouros) Mohammadian (UIC).

Powertrain Adoption Scenarios

Vehicle sales projected using NREL’s ADOPT model, based on AEO 2017 fuel prices and 

different technology improvement trends over time:

Conventional powertrain dominant 

0%

20%

40%

60%

80%

100%

20152020202520302035204020452050

Sales %

Year

ADOPT ScenarioBase Case

Gasoline

HEVs

BEVs

PHEVs

Plug-in electric vehicle powertrain dominant 

0%

20%

40%

60%

80%

100%

20152020202520302035204020452050

Sales %

Year

ADOPT ScenarioHigh Hybrid and Electric Vehicle Case

Gasoline

HEVs

BEVs

PHEVs

CACC Adoption

CACC VMT share on highways and freeways, 3 scenarios of CACC adoption:

0%

20%

40%

60%

80%

100%

20152020202520302035204020452050

VMT share %

Low Case

no CACC L1

CACC, L1

0%

20%

40%

60%

80%

100%

20152020202520302035204020452050

Mid Case

no CACC L1

CACC, L1

0%

20%

40%

60%

80%

100%

20152020202520302035204020452050

High Case

no CACC L1

CACC, L1

National-Level VMT 

Based on conflation of typical daily VMT 

from the Highway Performance 

Monitoring System (HPMS) with typical 

daily speed profiles from TomTom data:

Total VMT (in millions) distributed by road category, environment, and average driving speeds at the 

time of travel (considered indicative of congestion level)

Avg Speed Bins 

(mph)

Freeways & Highways Connectors & 

Arterials Local Roads

Rural Urban Rural Urban Rural Urban

[0-5] 0.000 0.000 0.004 0.020 0.013 0.123

(5-10] 0.006 0.046 0.148 1.123 0.350 3.147

(10-15] 0.019 0.172 1.267 8.469 3.290 17.752

(15-20] 0.040 0.375 3.553 22.210 5.324 34.868

(20-25] 0.092 0.679 7.287 43.720 5.992 33.297

(25-30] 0.183 1.466 13.926 74.978 9.742 38.947

(30-35] 0.339 3.160 23.217 114.512 13.668 48.795

(35-40] 0.466 5.173 30.301 129.852 14.877 47.928

(40-45] 0.668 11.947 36.814 116.873 15.699 30.652

(45-50] 0.951 24.784 45.680 84.444 12.975 12.415

(50-55] 1.863 52.048 58.591 54.800 12.835 4.803

(55-60] 4.956 114.023 95.089 50.712 12.506 1.868

(60-65] 16.907 207.692 67.158 55.630 2.516 0.332

(65-70] 62.286 186.095 62.429 34.555 0.080 0.001

(70-75] 95.927 42.591 8.523 2.249 0.000 0.000

(75-80] 4.802 0.328 0.002 0.000 0.000 0.000

>80 0.000 0.001 0.000 0.000 0.000 0.000

Total Number 

(from HPMS) 189.50 650.58 453.99 794.15 109.87 274.93

• Refine inputs and interactions with other tools

o VMT transferability from ANL/UIC (Chicago → nation)

o Microsimulation data outputs (trajectory data from local CACC 

implementation, automated mobility districts microsimulation, etc.)

• Sensitivity analysis to explore impact of several input parameters on the 

national-level fuel consumption results

• Add additional vehicle and CAV technology scenarios: 

o Explore national-level fuel consumption impacts of eco-signal 

implementation

o Explore national-level fuel consumption impacts of automated mobility 

districts and innovative mobility solutions

• Collaboration with other SMART Mobility pillars

o e.g., Urban Science, Advanced Fueling Infrastructure, etc.

Vehicle Fuel Consumption

Base year FC for all 

powertrains:

TSDC Data FASTSim 

Simulations

Speed Bins & 

Road Type 

Categorization 

Fuel 

Consumption 

Matrix • Real world drive 

cycles data, 

representative of 

US travel patterns

• Serve as input to 

FASTSim

• Model that 

estimates fuel 

consumption (FC)

• Link-based fuel 

consumption 

output from real 

world drive cycle 

data (input), for 

different vehicle 

powertrains & 

classes

• Conflation of 

FASTSim FC outputs 

with TomTom data 

-> determine road 

type and 

urban/rural 

environment of 

each link

• Categorization of 

FC by speed and 

road type 

• For each speed 

bin/road 

type/powertrain fit 

best distribution 

(normal) to come 

up with mean and 

std. deviation value 

of FC

•Generate colorcoded FC matrix by 

powertrain 

(base year)

e.g., Fuel consumptions pdf for 

[55-60] mph speed bin

Source: Shladover and Greenblatt. (forthcoming). Connected and Automated Vehicle Concept Dimensions and Examples. LBNL Report

e.g., Fuel consumptions for conventional 

vehicle (Toyota Camry) – analysis base year

Methodology described in detail in : Chen et al. (2018).

Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach.

Transportation Research Part A: Policy and Practice

FASTSim described in: Brooker, A., Gonder, J., Wang, L., Wood, E., Lopp, S., & Ramroth, L. (2015).

FASTSim: A model to estimate vehicle efficiency, cost and performance (No. 2015-01-0973). SAE Technical Paper.

Speed Bins (bin) / Time / CACC Penetration

(20-25]

Off-Peak

On-Peak

(25-30]

Off-Peak

On-Peak

(30-35]

Off-Peak

On-Peak

(35-40]

Off-Peak

On-Peak

(40-45]

Off-Peak

On-Peak

(45-50]

Off-Peak

On-Peak

(50-55]

Off-Peak

On-Peak

(55-60]

Off-Peak

On-Peak

(60-65]

Off-Peak

On-Peak

(65-70]

Off-Peak

On-Peak

....................

0

1

2

3

4

Fuel Consumption [gallons / 100 mi]

0K

2K

4K

6K

8K

10K

VMT

Time , CACC Penetration

Off-Peak, CACC 0%

Off-Peak, CACC 50%

Off-Peak, CACC 100%

On-Peak, CACC 0%

On-Peak, CACC 50%

On-Peak, CACC 100%

Fuel consumption impacts

• CACC reduces FC rates 

in most but not all speed 

bins

• Relative impact greater at 

low speeds, though the 

smaller impacts at high 

speeds apply to more 

VMT

VMT impacts

• Total VMT kept constant 

across CACC scenarios (no 

induced demand effect)

• CACC increases VMT in 

speed bins between 50 mph 

– 70 mph for on-peak and 45 

mph – 65 mph for off-peak 

conditions

Impact of CACC Penetration Levels on VMT and Fuel Consumption

LBNL microsimulation data outputs inform fuel consumption & VMT matrices under CACC adoption 

(note that VMT & FC correspond to the LBNL freeway network and not to the national level analysis) 

Conventional Vehicle Penetration Scenario High Electric Vehicle Penetration Scenario

2015 2020 2025 2030 2035 2040 2045 2050

Year

2015 2020 2025 2030 2035 2040 2045 2050

Year

20B

40B

60B

80B

100B

120B

140B

Gas Consumption [gallons]

National-level Fuel Consumption Projections

CACC VMT share

no CAVs

Low

Mid

High

National-Level Fuel Consumption Results

• Potential for gasoline fuel savings from CACC adoption, particularly when 

conventional powertrains dominate

Speed bins [mph]

CACC no CACC

[20-25][25-30][30-35][35-40][40-45][45-50][50-55][55-60][60-65][65-70][70-75][20-25][25-30][30-35][35-40][40-45][45-50][50-55][55-60][60-65][65-70][70-75]

2025 2040

0B

50B

100B

VMT

0B

50B

100B

VMT

Conventional Vehicles VMT in Urban Freeways

Mid CACC penetration case -

Base Scenario : Conventional Powertrains Dominate

Technology

CACC

no CACC

CACC Penetration Impacts on Conventional Vehicles

• VMT attributed to CACC increase over the years (urban highways example)

• Conventional fuel consumption decreases as CACC % increases (e.g., urban 

highways) 

Speed Bins (mph)

Year

2025 2035 2045

[20-25]

[25-30]

[30-35]

[35-40]

[40-45]

[45-50]

[50-55]

[55-60]

[60-65]

[65-70] 2.75

2.64

2.71

2.85

3.08

3.40

3.43

3.29

3.40

3.65

2.81

2.71

2.78

2.93

3.14

3.42

3.47

3.42

3.57

3.81

2.87

2.79

2.85

3.00

3.20

3.44

3.52

3.55

3.74

3.97

Conventional Vehicle FC Changes

Urban Highway

2.64 3.97

Gallons per 100 mi