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Study of the Potential Energy Consumption Impacts of Connected and Automated Vehicles

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Study of the Potential Energy Consumption Impacts of Connected and Automated Vehicles Study of the Potential Energy Consumption Impacts of Connected and Automated Vehicles March 2017 Independent Statistics & Analysis U.S. Department of Energy www.eia.gov Washington, DC 20585 This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the U.S. Department of Energy or other federal agencies. U.S. Energy Information Administration | Improving the Quality and Scope of EIA Data i March 2017 Introduction The U.S. Energy Information Administration (EIA) contracted with Z, INC. to analyze the potential energy consumption implications of connected and automated vehicle technologies on light-, medium-, and heavy-duty on- and off-road vehicles in the United States. Z, INC. subcontracted with Energetics Incorporated to review the state of automated vehicle technologies and project potential energy effects by: Conducting a literature review and interviewing key stakeholders on the current state and projected development of automated vehicles, applicable technologies, and regulations Discussing the potential implications of these technologies on future vehicle sales, usage, ownership, and energy consumption Developing an Excel-based model to project energy consumption effects of different adoption scenarios based on the Annual Energy Outlook 2017 Reference case Recognizing that connected and automated vehicle technologies and regulations are rapidly developing, Energetics recommended further study as more data become available. Suggested further study includes: Projection scenarios with all five levels of autonomy and possible ownership models available Exploring the effects of human factors on trust, adoption, and usage related to vehicle miles traveled, purchasing, and ownership strategy Improving the projections to include vehicle powertrain and fuel type EIA plans to incorporate into the upcoming Annual Energy Outlook 2018 a methodology for projecting the effects of connected and automated vehicles on energy consumption, sales, ownership, fuel economy, and related vehicle metrics for light-, medium-, and heavy-duty vehicles. EIA will use this report to help develop inputs and methodology to enable projection scenarios with varying degrees of connected and automated vehicle adoption. Further expansion and refinement will be possible as the technologies evolve and existing and future regulations develop in response to these technologies. U.S. Energy Information Administration | Improving the Quality and Scope of EIA Data 1 March 2017 Appendix A U.S. Energy Information Administration | Improving the Quality and Scope of EIA Data 2 Study of the Potential Energy Consumption Impacts of Connected and Automated Vehicles Prepared for: U.S. Energy Information Administration Prepared by: Energetics Incorporated Z, INC. Under Contract Number DE-EI0002498 Call Order DE-BP0005336 Submitted: February 22, 2017 Preface The U.S. Energy Information Administration (EIA) develops energy consumption data and projections (tables and reports) for the United States (U.S.) and the world. These publicly available sources provide extensive information about historical, current, and projected energy consumption (by fuel type) for personal use, industrial, and commercial applications. The transportation sector in the U.S. is a leading energy consumer, and relies primarily on gasoline- and diesel-powered vehicles for on-road travel. Connected and automated vehicles are under development and testing, and could impact U.S. transportation sector energy use. Autonomous vehicles have the potential to provide opportunities for vehicle manufacturers, users, and policymakers to establish a foundation for improved per-vehicle and transportation infrastructure use while reducing energy use on a mile-of-travel basis. The deployment of these vehicles, as well as their energy impacts, depends on the technologies offered, costs, influence of enabling and/or restrictive policies and legislation, and rate/nature of consumer adoption. This report considers the impacts of autonomous vehicles through 2050. Because of the early state of the industry and the high level of uncertainty, the 2026–2031 period (10–15 years in the future) was the primary focus for potential impacts. This report presents results from a comprehensive literature review as well as input from limited expert interviews about the technical, societal, and economic impacts of the deployment of autonomous vehicles. The study focused on determining the potential impacts of these vehicles on energy consumption over time via estimates of potential market penetration. Acknowledgements EIA led the conceptual development of this study. EIA would like to recognize the efforts of Z, INC. and Energetics Incorporated staff for conducting the research. Energetics Incorporated would like to acknowledge the industry experts who were interviewed during the research stage for their invaluable input to the project. i Executive Summary The U.S. Energy Information Administration develops, maintains, and operates the National Energy Modeling System (NEMS), a modular computer simulation model of the U.S. energy system. The Transportation Demand Module component of the NEMS represents energy consumption in the transportation sector dependent on prices, technology, policy, and other relevant factors. The overarching goal of this project was to estimate the impact of Connected and Automated Vehicles (CAVs) on transportation energy use in the United States through 2050. The project’s research examined CAV technology, commercial product availability, consumer/market adoption, driver behavior, and other mechanisms. Because of the early state of the industry and the high level of uncertainty, the focus was primarily on a 15-year horizon. The project results will inform EIA of CAV technology’s technical status and the potential impacts on transportation energy use for incorporation into the NEMS model. CAV technology information was collected through a comprehensive literature review that was augmented by telephone interviews with leading technology developers and researchers. The research results were used to develop this report and data summary spreadsheets and a companion transportation energy use projection model. Status of Automated Vehicles Many experts believe connected, automated, and autonomous vehicle technologies may trigger a revolution in personal transportation and mobility. These beliefs are based on the technology’s expected societal benefits relating to safety, convenience, reliability, and equity. Automated vehicles control the function of at least some aspects of a safety-critical control function (e.g., steering, throttle, or braking) without direct driver input. Autonomous vehicles, also referred to as “fully-automated” vehicles, are a subset of automated vehicles. Autonomous vehicles are capable of self-driving with limited or no connectivity with other vehicles or the infrastructure. The Society of Automotive Engineers International (SAE) categorizes automated driving functionality into distinct levels, summarized below by level of driver involvement during operation: Level 0 (Driver Only) – No automation; the human driver is responsible for all driving tasks. Level 1 (Assisted) – The automated system on the vehicle can assist the human driver within the defined use cases (i.e., operating environments and conditions) of the driving task. Level 2 (Partial Automation) – The automated system on the vehicle conducts multiple parts of the driving task. The human continues to monitor the driving environment and perform the remaining driving tasks. Level 3 (Conditional Automation) – The automated system conducts multiple parts of the driving task and monitors the driving environment within the defined use cases. The human driver must always be ready to take back control when the automated system requests. Level 4 (High Automation) – The automated system conducts the driving task and monitors the driving environment within the defined use cases. The human need not take back control when operating in these defined use cases. The human driver assumes control outside of the defined use cases. Level 5 (Full Automation) – The automated system performs all driving tasks within all use cases that a human driver could perform them. CAVs require a cooperative system of cameras, sensors (e.g., radar, LiDAR, and sonar), communications technologies (e.g., 5.9 gigahertz dedicated short-range radio communication), automotive technology ii (e.g., drive-by-wire systems), controllers, and advanced information capabilities (e.g., neural networks, machine learning, and artificial intelligence). Federal and State Policies The federal government is involved in this space via the National Highway Traffic Safety Administration (NHTSA), the Federal Highway Administration, and the Federal Motor Carrier Safety Administration, all of which are part of the U.S. Department of Transportation. There are currently no specific federal policies or regulations in place that govern (or restrict) the use, operation, or deployment of automated technologies for light-duty vehicles (LDV) or heavy-duty vehicles (HDV). In 2016 NHTSA issued a Notice of Proposed Rulemaking that would mandate vehicle-to-vehicle connectivity, a related
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