Loop through time for both equipped and non-equipped vehicles

Defining Inputs

CAV CapabilitiesBehavior Modeling

Fleet Makeup

Fleet Usage Urban Form

Traffic Modeling 

Infrastructure Characteristics 

Estimating Effects Modeling Interactions

Why: Foundational technology cases for potential impact scenariosInput assumptions: Technology/cost advancement ratesIterative influence from increased production volumes

Why: Capture how purchase and travel decisions may change in response to new technology availabilitySupporting data/tool examples: 

Vehicle uptake predictions using ADOPT, MA3T (consumer preference) or VICE, PReP, AFLEET (commercial net-present cost)Travel behavior predictions using opinion surveys (asking about hypotheticals) and data collection (revealed behavior changes from car sharing, managed lanes, early prototype deployments)

What: Track fleet composition, including penetration of new technologies and turnover rate for baseline/legacy vehicles

What: Track use intensity through time for both CAVs and conventional vehicles in the fleet under various scenarios

What: Potential larger time-scale evolution of urban layout (iterative with vehicle/fleet characteristics)

Supporting tools: “Four-S

travel-demand models, traland-use models, 4-D methland-use scenario planning 

Why: Travel profile and car-fobehavior differencesSupporting data/tool examBaseline calibration data from Trip generation analysis using Traffic flow/drive profile analycombination of travel choices between equipped and non-euse tools such as DRIVE, Vissim

Why: Infrastructure coordination/ interaction impacts scenariosInput assumptions: Road-side equipment (RSE) development and penetration ratesIterative influence from inter-dependent penetration of vehicles and infrastructure and any travel behavior shifts

Modeling the Energy Use of a Connected and Automated Transpor

Introduction and PurposeEarly research points to large potential impacts of connected and automated vehicles (CAVs) on transportation energy use – dramatic savings, increased use, or anything in between. [1,2] Due to a lack of suitable data and integrated modeling tools to explore these complex future systems, analyses to date have relied on simple combinations of isolated effects. This poster proposes a framework for modeling the potential energy implications from increasing penetration of CAV technologies and for assessing technology and policy options to steer them toward favorable energy outcomes. Current CAV modeling challenges include estimating behavior change, understanding potential vehicle-to-vehicle interactions, and assessing traffic flow and vehicle use under different automation scenarios. To bridge these gaps and develop a picture of potential future automated systems, NREL is integrating existing modeling capabilities with additional tools and data inputs to create a more fully integrated CAV assessment toolkit.Any effort to produce integrated modeling of such a complex and novel system will face challenges. These include selection of plausible inputs and handling of path dependence (what to model, in what order, and how to link disparate tools). When possible, data collection of related and early technology deployments should be used to validate each of the framework’s modules.

Possible ApplicationsThis first-of-a-kind toolkit will allow NREL and its research collaborators to address new and critical questions affecting the future of automation:• What are the energy opportunities at the 

different levels of automation, from level 1 (function-specific automation) to level 4 (full self-driving automation)? [3] 

• What factors most affect which path 

automation might take?

• What factors most affect the rate of CAV 

adoption and concurrent changes to the transportation system?

• Do we expect “tipping points” where system 

behavior changes qualitatively depending on CAV penetration and automation algorithms?

• How could system infrastructure deployment 

for vehicle connectivity influence CAVs in the transportation system?

• What other energy technologies may either 

benefit from or facilitate increased automation?

This work was supported by the U.S. Department of Energy’s Office of Strategic Programs and the National Renewable Energy Laboratory.

0 

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1 

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0 20 40 

Index (relative to age 40) 

Ag

Relative Travel by Segm

Petroleum ConsumpƟon

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vehicles (PEVs)

n CAV hybrid electric 

vehicles (HEVs)

n CAV convenƟonal 

vehicles (CVs)

n Connected PEVsn Connected HEVsn Connected CVsn PEVsn HEVsn CVs

10

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