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Assessing the Energy Impact of 

Connected and Automated 

Vehicle (CAV) Technologies

SAE 2016 Government/Industry Meeting

January 21, 2016

Jeff Gonder, Yuche Chen, Mike Lammert, Eric Wood

Transportation and Hydrogen Systems Center (THSC)

National Renewable Energy Laboratory (NREL)

NREL/PR-5400-65743
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Outline

• Overall energy impact assessment

• Example feature-level impacts

• Real-world/off-cycle benefit calculation

• On-going work by DOE and its national labs
3

“Bookending” CAV Energy Impact Analysis

• Identified dramatic potential energy impacts (across automation levels)

o Informed by related NREL work and literature review

o Significant uncertainties remain; further research warranted/on-going

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Fuel Intensity Energy Intensity Use Intensity

Platooning

Efficient driving

Efficient routing

Travel by underserved

Full cycle smoothing

Faster travel

More travel

Lightweighting & 

powertrain/vehicle size 

optimization

Less hunting for parking

Higher occupancy

Enabling 

electrification

Positive Energy Outcomes Negative Energy Outcomes

0 500 1000 1500 2000 0

10

20

30

40

50

60

Seconds

MPH

Actual

Ideal

Implications for advanced 

powertrains and vehicle design

Travel demand impacts

Brown, A.; Gonder, J.: Repac, B. (2014). “An Analysis of Possible Energy Impacts of Automated Vehicles.” Chapter 5, Societal and Environmental 

Impacts. Meyer, G., ed. Lecture Notes in Mobility: Road Vehicle Automation. Berlin: Springer.
4

Example from Collaborative Project with GM on Green 

Routing and Adaptive Control for the Chevy Volt

Drive 

Cycle 

Model

Volt PT 

Model

Road Type

Real-time Traffic

Driver Aggression

Cycle Metrics

Road Grade

Vehicle State

Estimated 

Energy Use

Candidate

Routes

NREL/GM Algorithms

• Computationally heavy to develop

o Hundreds of thousands of drive cycles processed, 

analyzed, and simulated

• Computationally light to implement in-vehicle

o Does not require determination of time/speed trace or 

real-time simulation of high-fidelity vehicle model

Gonder, J.; Wood, E.; Rajagopalan, S. “Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt.” 

Proceedings of the 21st World Congress on Intelligent Transport Systems, Sept 2014. www.nrel.gov/docs/fy14osti/60960.pdf
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Green Routing Example

O

*Normalized Values

D

Route A B C

Distance, mi 81.6 76.2 67.6

Duration, min 107 107 113

Avg Elec Rate, 

Wh/mi* 0.83 0.89 1.0

Avg MPG* 0.45 0.50 1.0

Cost, $* 1.0 0.89 0.59

Origin

Destination
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Collaborative 

Project Summary

• Demonstrated ability to 

model vehicle speed/accel

profiles relative to road type

• Constructed high-level 

powertrain model employing 

cycle metrics and vehicle 

state as inputs

• Applied model using real-

world distribution of O/D 

pairs, demonstrating: o Aggregate energy savings of 

up to 4.6% for green routing 

(relative to passenger value of time)

o Average energy savings of 

3.3% for mode scheduling

NREL IG 18563

Modest aggregate 

savings, but may be 

cost effective
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Evaluating Truck Platooning Efficiency Benefits

• Many factors can 

influence

o Vehicle spacing

o Cruising speed

o Speed variation

o Baseline

 aerodynamics

o Vehicle loading

o Engine loading

• Also potential safety and 

comfort benefits

Results from SAE Type II track testing of Peloton 

Technology system over a variety of conditions

Photo from Mike Lammert, NREL Lammert and Gonder poster: www.nrel.gov/docs/fy14osti/62494.pdf

Lammert, et al. SAE Int. J. Commer. Veh.: www.nrel.gov/docs/fy15osti/62348.pdf
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Related Analysis Effort on Real-World Efficiency Benefits

• Evaluate real-world fuel-saving opportunities for technologies 

difficult to assess with standard certification cycles

o DOE and regulatory bodies want to maximize real-world fuel savings

o Manufacturers want to get credit for actual fuel savings achieved

• Strong interest from multi-lab/OEM workgroup under U.S. DRIVE; 

example technologies:

o Engine encapsulation

o Start-stop

o High-efficiency alternators

o High-efficiency lighting

o Glazing technology

o Connected vehicle applications

• DOE labs such as NREL can provide objective inputs

• Relevant existing capabilities

o Evaluation of energy efficiency technologies

o On-road driving data

o Fusion of large datasets capturing range of real-world operating conditions

OEM = original equipment manufacturer

US DRIVE = Driving Research and Innovation for Vehicle efficiency and Energy sustainability
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Transportation Data Centers at NREL

Alternative Fuels Data Center (AFDC)

Public clearinghouse of information on the full range of advanced vehicles and fuels

National Fuel Cell Technology Evaluation Center (NFCTEC)

Industry data and reports on hydrogen fuel cell technology status, progress, and challenges

Transportation Secure Data Center (TSDC): Detailed individual travel data, including GPS profiles

Fleet DNA Data Collection

Medium- and heavy-duty drive-cycle and powertrain data from advanced commercial fleets

FleetDASH: Business intelligence to manage Federal fleet petroleum/alternative fuel consumption

Real-World Data and Analysis to Support Decision Making

Features AFDC NFCTEC TSDC Fleet

DNA

Fleet

DASH

Securely Archived Sensitive Data Y Y Y Y

Publicly Available Cleansed Composite Data Y Y Y Y

Quality Control Processing Y Y Y Y Y

Spatial Mapping/GIS Analysis Y Y Y Y Y

Custom Reports Y Y Y

Controlled Access via Application Process Y

Detailed GPS Drive-Cycle Analysis Y Y
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Integration with Other Large Datasets

Elevation / Grade Ambient Temperature

Vehicle Registrations Solar Intensity

Freight Volumes

Overall Road Volumes

GPS Travel/Drive Cycles Digital Street Maps Traffic Speeds
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Prototyped Process for National-Level Aggregation of 

“Off-Cycle” Technology Impacts - 1

Use test data plus modeling to determine a given vehicle’s fuel consumption 

rate over a range of driving situations

• Consider drive profile characteristics, road grade, temperature, solar load, etc.

• Use large real-world driving database to correlate drive profile characteristics 

with road type/traffic conditions

Fuel Consumption Rates

Road Type 

Correlation

Thermal Impacts Grade Impacts

Drive Profile 

Characteristics
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Prototyped Process for National-Level Aggregation of 

“Off-Cycle” Technology Impacts - 2

Combine national datasets on driving volumes by road type, climate 

conditions, road grades, etc.

• Proportionally weight consumption rates from different situations by the amount 

of driving each represents across the country and a typical meteorological year

• Calculate aggregated national-level fuel economy

Vehicle Miles Traveled (VMT) Volumes
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Prototyped Process for National-Level Aggregation of 

“Off-Cycle” Technology Impacts - 3

Repeat process with and without a given off-cycle technology enabled to 

calculate its national-level benefit

• Methodology captures varying impacts a technology can have across a broad 

range of driving conditions

• Aggregation process permits the national-level A/B comparison
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Multi-Lab Project on Energy and GHG Implications from CAVs

• ANL, INL, NREL and ORNL participating

• Recognizing large potential impacts and uncertainties

o Potential disruption of travel patterns, vehicle use, ownership and 

even design

• Seeking to refine bounds on potential energy 

consumption implications at the U.S. national level

o Assess specific scenarios

o Implement/refine national-level aggregation methods

• Identify key considerations for encouraging beneficial 

energy outcomes and for mitigating adverse energy 

outcomes

GHG = greenhouse gas

ANL/INL/ORNL = Argonne/Idaho/Oak Ridge National Laboratories
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Transportation as a System

Today: 

• Vehicle-level focus

• Independent

• Unconnected

• Subject to behaviors & decisions

Tomorrow: 

• System-level focus

• Connected 

• Automated

• In concert 

• Across modes 

• Managed behaviors & decisions

Exploring the untapped transportation system-level efficiencies
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Focus Areas (“Pillars”)

Focus Area Future New Technologies/Models/Knowledge

Mobility 

Decision Science

• New knowledge and applications of decision science to collect and analyze 

real-world data on transportation decision making, electric and alternative 

fuel vehicle market drivers and barriers, as well as new mobility options. 

Connectivity 

and Automation

• An increased understanding of the potential impact of connected and 

automated vehicles and their implications on transportation and vehicle 

technologies, such as electrification and overall mobility. 

Multi-Modal • Dynamic passenger/freight modal energy-intensity modeling with explicit 

consideration of consumer/market preferences and energy implications.

Urban Science

• Integrated city-scale models that explicitly consider energy impacts of 

urbanization by collecting real-world data and collaborating with local 

governments.

Vehicles and 

Infrastructure

• Integrated vehicle-fuel models to explore value propositions (consumer and 

provider), business models and opportunities for increased sustainable 

transportation deployment.
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What’s the plan?

FY15-FY16

Foundational efforts at

DOE National Labs

Notional DOE

-supported effort

FY17

Ramped-up efforts

at Labs with partners

FY18+

Large-scale with

multiple performers

Phase 1 Phase 2 Phase 3
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Transportation as a System: What’s Next...?

DOE-funded multi-lab consortia:

1) Engage with key stakeholders, including the 

U.S. Department of Transportation (DOT), key 

universities with transportation research 

centers, and major cities and/or regions with 

ongoing DOT-funded efforts on mobility; 

2) Design and execute robust analytical and 

foundational efforts to define and build-up 

constituent parts and frame DOE priority 

opportunities; and 

3) Identify opportunities for focused technology 

demonstrations in conjunction with cities or 

states to spur commercialization and inform 

future activities across DOE’s transportation 

technology portfolio.
Questions?

NREL Slides:

Jeff Gonder, jeff.gonder@nrel.gov, 303-275-4462

Federal POC: Jacob Ward, 

jacob.ward@ee.doe.gov, 202-586-7606
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Appendix
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Discussion Point: Many CAV technologies may require such a realworld/off-cycle assessment approach

• E.g., efficient routing, cycle smoothing, 

and adaptive control technologies

• Assess energy benefit from potential realworld change, and frequency of 

occurrence

• Could utilize existing pathway for 

demonstrating off-cycle credit beyond 

pre-defined table of technologies
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Thoughts on Automation/Electrification Synergy

• Automation easier with electrified driveline

• Information connectivity helps with vehicle/grid integration

• Automated alignment for wireless power transfer (WPT)

• Automated plug-in electrified vehicle parking/charging

o Value from valet anywhere, maximized electrified miles and infrastructure 

utilization, minimized anxiety about range and finding chargers

• Vehicle right-sizing for trip/range

Acknowledging some caveats

• Can also automate conventional vehicle powertrains to obtain ondemand valet and taxi benefits; also raise efficiency baseline

• Shared-use automated taxis may have lengthy daily ranges

o But improvements in battery cost, fast charging, WPT could still enable 

electrification

o Also note operating cost/efficiency may become more important for such 

vehicles
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• Multi-powertrain real-world fuel economy distributions/sensitivities

• Comparing real-world driving and standardized test profile results

• Enabling road grade simulation and quantifying impacts

• Synthesis with national climate data for thermal technology evaluation

• Investigating PEV charging and alternative fuel station locations

• Developing green routing and adaptive control algorithms

• Assessing fuel saving opportunities from driver feedback...

Extensive NREL Analyses with Large GPS Datasets in the TSDC
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Photo Credits

• Slides 3 and 7 – Truck photo by Mike 

Lammert, NREL