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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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DOE SMART Mobility Urban Science Efforts are helping use/develop key data sets, models, and roles for DOE in engaging across seven Smart City Finalists as well as for ensuring useful/useable insights. Austin, Texas, was targeted for analyses of opportunity (model and data maturity). Other Urban Science/broader SMART initiatives were also helped by this research.
Here, this paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m (m ≤ n) trips. This new assignment system has been deployed and integrated into the POLARIS. Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).
Presentation about NREL's work related to integrating electric vehicles (EVs) in the electricity system. Presented at the United Nations Industrial Development Organization (UNIDO) Expert Group Meeting on 'Smart Mobility for Smart Cities' in Vienna Austria, September 2019.
Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: (1) What are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? (2) Which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? (3) To what degree can micromobility supplement/complement transit system operations? (4) What are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? (5) What are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: (1) Energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel. (2) Multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations. (3) Mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis. (4) Energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams. (5) Micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.
Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: 1) what are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? 2) which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? 3) to what degree can micromobility supplement/complement transit system operations? 4) what are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? 5) what are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: 1) energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel; 2) multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations; 3) mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis; 4) energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams; 5) micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.
When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.
This study is part of the US Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility initiative. It is included in the Multi-Modal pillar of SMART Mobility and is titled: Optimization of Intra-City Freight Movement and New Delivery Methods. The primary purpose of this study is to analyze the energy reduction opportunities provided by new freight modes and technologies. This will be done by developing a tour-based freight model for Columbus, Ohio. This model will be used to analyze the energy and emission savings of alternative technologies including drones, central delivery lockers, and the use of electric vehicles for last-mile freight deliveries. The tour-based model will also be used to examine the growth and distribution of freight traffic and its impacts on the local transportation system. Development of this model will be coordinated with the Mid-Ohio Regional Planning Commission (MORPC), the Metropolitan Planning Organization (MPO) for the Columbus, Ohio area.
This presentation is for the Plenary Session: "Unmanned Traffic Management (UTM) and the Future of Unmanned Systems in Urban Airspace" at the ASCE flagship 2019 International Conference on Transportation and Development, "Engineering Smart Mobility for the Smart City", June 9-12, 2019. The Panel is at 8:00 am on Wednesday, June 12 and moderated by Brent Ingraham (DOD). This presentation supplements Dr. Kopardekar's short introduction on how NASA is developing an Unmanned Traffic Management system and how this UTM will change the future of shared airspace.
This presentation focuses on the potential impact of connected and automated technologies on commercial vehicle operations. It includes topics such as the U.S. Department of Energy's Energy Efficient Mobility Systems (EEMS) program and the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Initiative. It also describes National Renewable Energy Laboratory (NREL) research findings pertaining to the potential energy impacts of connectivity and automation and stresses the need for integration and optimization to take advantage of the benefits offered by these transformative technologies while mitigating the potential negative consequences.
Mobility is one of the fundamental aspects of human behavior, governed by factors such as time, cost, convenience, and availability of travel options. A new measurement developed by NREL researchers, called the Mobility Energy Productivity metric, provides an avenue to not only measure the quality of mobility at a specific location in its current configuration, but also to test how various technological advancements (e.g. connected and automated vehicles (CAVs), plug-in electric vehicles, shared mobility) and infrastructure investments (e.g. building an additional highway lane; constructing a new shopping mall) may impact the mobility of that location over time. A location with high quality mobility offers multiple transportation options to a diverse number of opportunities while minimizing time, cost, and energy consumption - all factors which define a high quality of life. Development of the MEP metric is a cornerstone of the U.S. Department of Energy's (DOE) SMART Mobility Laboratory Consortium. The metric will aid in developing new knowledge, insights, tools, and technology solutions about the evolving connected mobility system, thereby informing decision makers about how emerging mobility choices could impact people's lives.
Increasing interest and investment in connected, automated, and electric vehicles (CAEVs), and mobility-as-a-service (MaaS) concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities regarding benefits or dis-benefits of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This presentation provide the details of a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as Automated Mobility District (AMD) modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions.The AMD modeling and simulation task is funded through DOE's SMART Mobility Consortium. This presentation outlines the progress of the AMD modeling and simulation task in FY19, including case studies conducted in Greenville, SC, and Austin, TX.
Presentation describes project to develop a framework for access to large datasets (i.e., millions of database objects, dynamically calculated values, or raster datasets that exceed 100s of Megabytes) from smart mobile devices with dynamic network bandwidths.
This presentation summarizes the objectives, approach, and findings of a 3-year project led by INL as part of DOE's SMART Mobility Laboratory Consortium. The presentation will be given to the general public via Zoom during the FY20 Vehicle Technologies Office Annual Merit Review on June 3, 2020.
Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.
Wireless power transfer is a safe and convenient method for electric vehicle (EV) charging. And, if designed properly, it can be highly efficient. Applications cover a wide range of power transfer levels from less than a kilowatt to a few hundreds of kilowatts with transfer distances from a few millimeters to a few hundreds of millimeters. Applications also cover stationary, dynamic (in-motion), or quasi dynamic wireless charging systems. Safety is inherent in wireless power transfer systems due to the large airgap separation between the source and load sides, and users do not have to touch any cables, connectors, or plugs at all. With the recent advancements in automation, parking aid systems can be integrated and the wireless charging process can be a simple “park and go” approach without requiring any input from the driver.When applied in motion, wireless charging can be the ultimate end of game in transportation electrification, providing reduced battery size and cost with unlimited range for EVs. This approach is also known as ”charge-on-the-go” or “roadway powered vehicle” concept. While the connected and automated vehicles can self-drive and self-park, they currently cannot self-charge. Furthermore, wireless charging is a key enabling technology for the widespread development and adoption of connected and automated vehicle technology for the future of smart mobility.
DEPRECATED This repository was archived by the owner on Jun 30, 2026. It is now read-only. The Connected and Automated Vehicles Scenario Generation (CSG) model is a system dynamics model. It was built by NREL and funded by the U.S. Department of Energy under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility initiative. The purpose of the model is to simulate the "transitions from predominantly individual ownership of non-CAVs to various future scenarios of high connectivity/automation"(Bush, Vimmerstedt, and Gonder 2019).
Vehicle emission is a major source of air pollution. A significant number of deaths is associated with air pollution. The existing vehicle emission monitoring system is unable to mitigate the pollution properly. Real-time pollution measurement is necessary to address the problem more precisely. The purpose of this project is to develop novel Vehicle Emission Monitoring Applications (VEMA) using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. As a part of the project, 2 technologies have been used for exhaust gas monitoring. First, an off-the-shelf exhaust gas monitoring prototype has been acquired. Second, a brand new platform was developed using off-the-shelf components. The project focused on predictive application for smart mobility and for large large fleet companies and enterprises. A data collection methodology was developed and tested by collecting close to 20 hours of data within Washington State under varying speeds, altitudes, road conditions etc. An approach for data curation was also used and finally, a simple Machine Learning was used for recognizing patterns between inputs such as vehicle speed and altitude and output such as NOx emission from vehicle exhaust pipe.
In an important step toward advancing intelligent, efficient, and autonomous electric vehicles, the National Renewable Energy Laboratory (NREL) plans to partner on research and development efforts with EasyMile, a smart mobility solutions company. EasyMile provided a demo of its driverless EZ10 shuttle on the NREL campus in September 2017.