SMART Mobility Connected and Automated Vehicles Capstone Report

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SMART Mobility

INL XXX-XX-XXXXX

Revision X [only after initial release]

Connected and Automated Vehicles Capstone Report

July 2020

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CONNECTED AND AUTOMATED VEHICLES CAPSTONE REPORT

Foreword

The U.S. Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019 and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Connected and Automated Vehicles (CAVs) Pillar. This Pillar investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant Pillar's Capstone Report.

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Acknowledgments

This material is based upon work supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), specifically the 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.

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of its employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

The following DOE Office of Energy Efficiency and Renewable Energy (EERE) managers played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance: David Anderson, Michael Berube, Erin Boyd, Heather Croteau, Prasad Gupte, and Jacob Ward.

The Connected and Automated Vehicles Pillar acknowledges the contributions of the project principal investigators and contributors:

Eric Rask,3 Joshua Auld,3 Brian Bush,2 Yuche Chen,2 Vincent Freyermuth,3 David Gohlke,3 Jeff Gonder,2 Jeffery Greenblatt,5 Jihun Han,3 Jake Holden,2 Ehsan Islam,3 Mahmoud Javanmardi,3 Jongryeol Jeong,3 Dominik Karbowski,3 Namdoo Kim,3 Mike Lammert,2 Paul Leiby,4 Zhenhong Lin,4 Xiao-Yun Lu,5 Kouros Mohammadian,6 Amir Parsa,6 Jackeline Rios-Torres,4 Aymeric Rousseau,3 Ramin Shabanpour,6 Steven Shladover,5 Daliang Shen,3 Matthew Shirk,1 Tom Stephens,3 Bingrong Sun,2 Omer Verbas,3 Chen Zhang.2

Affiliation during research effort: 1 Idaho National Laboratory 2 Renewable Energy Laboratory 3 Argonne National Laboratory 4 Oak Ridge National Laboratory 5 Lawrence Berkeley National Laboratory 6 University of Illinois at Chicago

This work was authored for the U.S. Department of Energy (DOE), by Lawrence Berkeley National Laboratory under Contract No. DE-AC02-05CH11231, Argonne National Laboratory under Contract No. DEAC02-06CH11357, Idaho National Laboratory under Contract No. DE-AC07-05ID14517, National Renewable Energy Laboratory under Contract No. DE-AC36-08GO28308, and Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725. Funding provided by U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office.

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Abbreviations

AADT ABM ACC ADAS ADS ADT AEO AES AEV AFDC AFI API ASCM ATM AV

average annual daily traffic agent-based -models adaptive cruise control advanced driver-assistance system automated driving system average daily traffic Annual Energy Outlook automated electric shuttle automated electric vehicle Alternative Fuels Data Center Advanced Fueling Infrastructure application programming interface active safety control module active traffic management automated vehicle

BAU BEV

business-as-usual battery electric vehicle

CACC CAN CAV CC CGM CMIP C-rate CRM

cooperative adaptive cruise control control area network connected and automated vehicle cruise control central gateway module charging management and infrastructure planning charging rate coordinated ramp metering

DOE DSRC

U.S. Department of Energy dedicated short range communication

EAD E-CAV ECU EIA EPA EV

eco-approach and departure electrified connected and automated vehicles engine control unit Energy Information Administration U.S. Environmental Protection Agency electric vehicle

FHWA Federal Highway Administration FMVSS Federal Motor Vehicle Safety Standards

GHG GNSS GPS

greenhouse gas Global Navigation Satellite System Global Position System

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HD HDCAV HDV HEV HPMS HWFET

heavy-duty connected and automated heavy-duty vehicle heavy-duty vehicle hybrid electric vehicle Highway Performance Monitoring System Highway Fuel Economy Test

I2V ICE ICEV

infrastructure-to-vehicle internal combustion engine internal-combustion-engine vehicle

KNN

K-nearest neighbors

LCV LD LDCAV LDV LRRM

long combination vehicle light-duty connected and automated light-duty vehicle light-duty vehicle local responsive ramp metering

MEP MiM MOE MPO MV MY

mobility energy productivity man-in-the-middle measure of effectiveness Metropolitan Planning Organization manually driven vehicle model year

NGSIM NHTS NREL

Next Generation Simulation national household travel survey National Renewable Energy Laboratory

ODD

operational design domain

PCM PEV PHEV PID PMT

powertrain control module plug-in electric vehicle plug-in hybrid electric vehicle proportional-integral-derivative controller passenger-miles traveled

RF

random forest

SAES SAEV SAVs SMART SPaT

shared automated electric shuttle shared automated electric vehicles shared automated vehicles Systems and Modeling for Accelerated Research in Transportation signal phase and timing

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TCO TMC TNC TRR TSDC TTD TTT

total cost of ownership traffic management center transportation network company Transportation Research Record Transportation Secure Data Center total travel distance total travel time

UDDS UMR UMS U.S.

Urban Dynamometer Driving Schedule Urban Mobility Report Urban Mobility Scorecard United States

V2I V2V V2X VMT VOTT VSL VSL/VSA

vehicle-to-infrastructure vehicle-to-vehicle vehicle-to-anything vehicle miles traveled value of travel time variable speed limit variable speed limits/variable speed advisories

ZOV

zero occupancy vehicles

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Executive Summary

Connectivity and Automation Can Lead to Significant Changes to Both Overall Efficiency and Usage

The continued growth of connected and automated vehicle (CAV) technologies is anticipated to significantly change the way vehicles move and the way travelers achieve mobility. This will have a significant impact on energy consumption, as well as many other facets of transportation, at scales ranging from the individual vehicle level to the transportation system level. CAV technologies are unique in that they can positively and negatively impact efficiency (both energy efficiency and passenger/freight efficiency) as well as vehicle miles traveled (VMT) and related metrics. In addition, the interactions between connectivity and automation-enabled technologies and the powertrain technologies to which they are applied will determine whether the impact of a particular technology solution (CAV or powertrain) will be positive or negative, based on the synergies between the new operating profile afforded by the connectivity and automation and the specific powertrain under analysis. For example, the energy benefits of regenerative braking in electrified powertrains are decreased in an environment where most vehicle transients are removed through improved vehicle cooperation and profile smoothing.

Before the initiation of the DOE SMART Mobility Laboratory Consortium, the Department of Energy conducted a study to address the ranges (bounds) of potential effects of CAV technologies on VMT and vehicle fuel efficiency.1 That report laid the foundation for the subsequent SMART Consortium efforts discussed in this report and identified research gaps to be assessed in greater detail by the consortium's efforts. Based on a review and synthesis of existing CAV literature, the bounding study concluded that there is enormous uncertainty about potential impacts on long-term VMT and efficiency if fully automated and highly connected vehicles replace nearly all light-duty passenger vehicles in the United States. (The bounding study used 100% penetration of new technologies for each assessment case.) Scenarios representing the lower and upper bounds of changes in fuel use for a national fleet of conventional powertrain vehicles were assessed, with projected energy consumption decreasing by as much as 60% of current U.S. light-duty vehicle (LDV) fuel consumption or increasing as much as two times (200%). The wide range between the lower and upper bounds of future vehicle energy use reflects the large uncertainties regarding ways that CAVs can potentially influence vehicle efficiency and use through changes in vehicle design, purpose of use, driving, travel behavior, management, and policies. The report grouped the factors impacting national-level fuel consumption into three primary categories: vehicle fuel consumption per mile, travel demand or VMT, and CAV adoption. Within these primary categories, the report also highlighted the most important data and knowledge gaps identified by the literature synthesis and impact analysis: 1) travel demand impacts due to automation and connectivity, 2) CAV adoption and market dynamics, 3) fuel (and energy) efficiency impacts due to connectivity and automation, and 4) connectivity and automation insights for heavy-duty vehicles. Aided by this foundational research work, the CAVs Pillar within DOE's SMART Mobility Consortium investigated the above key research priorities while also understanding synergies with connectivity and automation for both conventional and electrified vehicles.

U.S. Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Connected and Automated Vehicle Pillar

CAV technologies offer the potential for improving vehicle efficiency and possibly reducing overall transportation energy use through improved control and optimization, from the vehicle level to the corridor level and up to the city/regional scale. Connectivity and automation may also stimulate further vehicle electrification due to more convenient, transparent, and informed BEV charging and usage. However, the various levers for increased vehicle and transportation efficiency are at risk of being negated by an increase in VMT, due to possible rebound effects (driving more because connectivity and/or automation makes it easier and/or cheaper) as well as new use cases. Therefore, a systems-centric research effort is necessary to more

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