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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Managing Increased Electric Vehicle Shares on Decarbonized Bulk Power Systems

Transportation electrification and power sector decarbonization - the convergence of these two trends present a complex planning problem requiring realistic, region-specific modeling and analysis to ensure that both can happen rapidly and cost effectively. This project, funded through the U.S. Department of Energy's Vehicle Technologies Office, models the evolution of the U.S. bulk power system (through 2050) in response to large-scale EV charging across all on-road vehicle segments. We will assess the opportunity and value of demand-side flexibility (i.e., "smart" charging) for reducing energy costs, increasing renewable generation shares, and managing future EV loads on the bulk power system. Overall, this study aims to provide an improved understanding of least-cost solutions for managing EV load growth on the grid and will make high-resolution EV load data sets publicly available for further analysis.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Safety Codes and Standards Review and Gap Analysis for Applicability to Hydrogen Rail Refueling, Defueling, Maintenance, and Storage

Hydrogen-powered locomotives may present an alternative to diesel for achieving transportation-related decarbonization, energy security, and resilience goals. As an emerging technology, hydrogen locomotives may not have adequate representation in current safety codes and standards. NFPA 2 and other appropriate codes and standards were reviewed to identify technical gaps in the code for hydrogen rail defueling, refueling, maintenance, and storage. Several technical gaps pertaining to setback distances, allowable quantities, ventilation rates, and grounding have been identified. Fueling requirements can be informed by road vehicle standards but may need to be revised for larger quantity hydrogen locomotive systems. Since there are no hydrogen-specific locomotive design and safety standards, some aspects of diesel-based locomotive standards may apply. CGA G- 5.5 currently provides the most guidance on height, placement, orientation, and design of vent systems, while other standards emphasize the importance of shielding vent stacks and provide requirements for orientation of their discharge.

08 HYDROGEN↗
DOE OSTI2021

Propane Basics

Propane powers nearly 60,000 vehicles in the United States. Fleets around the United States have successfully implemented propane vehicles in many types of applications, including school buses, shuttle buses, step vans, and law enforcement vehicles. Propane is also frequently used in off-road applications, such as forklifts, commercial landscape mowers and equipment, and other farm equipment. The advantages of propane include its domestic availability, performance, and emissions benefits.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Thermodynamics and transport in molten chloride salts and their mixtures

Relationship between thermophysical properties and phonon mean free path. Heat capacity, viscosity, and thermal conductivity in ionic liquids decrease as mean free path decreases and dynamics become less “solid-like” and collect motion diminishes.

Cockrell, C. [Nuclear Futures Institute, Bangor Un↗
DOE OSTI2021

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Multiscale and Multivariate Transportation System Visualization for Shopping District Traffic and Regional Traffic

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. Additionally, these techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Commuting again to work on-site? Lab’s first drivers faced their own challenges decades ago

After a long pause in commuting, more and more Lab staff are filling travel mugs, topping off their tanks, and heading up the hill for on-site work. As long as the Lab has existed, workers have snaked through tight mountain roads to get to their jobs. And, back in the early 1940s, drivers faced even more challenges. In the Lab’s earliest years, drivers likely worried about getting stuck in the mud, navigating intense switchbacks, or making sure important equipment, such as a disassembled power plant, didn’t fall off the back of the truck that was hauling it. Creative solutions could go a long way to get out of a jam. As many of us return onsite and get reacquainted with commuting, we look back at how transportation has changed since the Manhattan Project, the U.S. government’s top secret effort at Los Alamos to create the first atomic bombs to help end World War II. These stories and photos are part of the collections in the National Security Research Center, which is the Lab’s classified library and also houses unclassified artifacts from our past.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Decentralized and mobile biomass torrefaction to upgrade the biomass supply chain

As part of the Cyclotron Road program, Takachar seeks to investigate the scaling laws and behaviors of a small-scale biomass torrefaction reactor design and its technoeconomic feasibility in various potential applications. Traditionally, biomass is logistically expensive and complicated to utilize in various energy and chemical industries, because it is often loose, wet, bulky, and present in small pockets in dispersed regions. Biomass torrefaction is an approach whereby raw biomass is subject to moderate heat, releasing low-energy molecules. The resultant torrefied biomass becomes energy and volume dense, which reduces the relative transportation cost, and in many cases can even support a local value chain that would otherwise not be possible. However, conventional biomass torrefaction systems are often large-scale, centralized in design, rending them difficult to deploy in many biomass applications in rural/decentralized regions. By building upon prior research at Massachusetts Institute of Technology (MIT), the concept of small-scale, low-cost, and portable biomass torrefaction units was explored further. This investigation has led to the advancement and scale-up of the MIT technology which can be deployed to a much wider spectrum of the biomass market than the conventional technologies.

09 BIOMASS FUELS↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2025

Enhanced Lighting Signals for Safety and Efficiency - Experiments With Addressable LEDs

For enhanced roadway safety, clear and immediate visual cues are essential for preventing accidents between drivers and pedestrians. At intersections, however, the line of sight to other roadway users may be obstructed by vehicles and infrastructure. Additionally, adverse conditions, including low visibility, poor weather, or inadequate lighting can increase the potential for collisions. Distracted drivers and pedestrians can further exacerbate the risk of accidents, particularly when using a smartphone, rather than focusing on roadway surroundings. These issues demonstrate the need for infrastructure upgrades that enhance visibility and awareness at crosswalks. A potential solution is through enhanced lighting signals integrated into the roadway infrastructure. One such example is the use of addressable LEDs, individually controllable lights that can change color and brightness instantaneously through programmable microcontrollers. They can be installed and integrated into crosswalks to maintain visibility in conditions where pedestrians may be difficult to see, while also offering peripheral cues to pedestrians who may be distracted by their phones or other objects rather than the road. Such a system (as one example) that is integrated into a traffic intersection digital twin that tracks all roadway users accurately, has the potential to enhance visibility of vulnerable road users, and thus enhance safety. This paper examines the potential implementation and feasibility of this technology, as well as the safety benefits it could provide. Laboratory experiments with addressable LEDs reveal the capabilities and challenges of this technology for roadway infrastructure safety. These findings could pave the way for more integrated lighting in infrastructure for vehicles and pedestrians at intersections, merge and diverge locations, and other areas where complex interactions present safety hazards. Such lighting solutions, enabled by modern computation and communications, could enhance safety and efficiency in our transportation system and improve overall mobility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Enhanced Lighting Signals for Safety and Efficiency - Experiments with Addressable LEDs

For enhanced roadway safety, clear and immediate visual cues are essential for preventing accidents between drivers and pedestrians. At intersections, however, the line of sight to other roadway users may be obstructed by vehicles and infrastructure. Additionally, adverse conditions, including low visibility, poor weather, or inadequate lighting can increase the potential for collisions. Distracted drivers and pedestrians can further exacerbate the risk of accidents, particularly when using a smartphone, rather than focusing on roadway surroundings. These issues demonstrate the need for infrastructure upgrades that enhance visibility and awareness at crosswalks. A potential solution is through enhanced lighting signals integrated into the roadway infrastructure. One such example is the use of addressable LEDs, individually controllable lights that can change color and brightness instantaneously through programmable microcontrollers. They can be installed and integrated into crosswalks to maintain visibility in conditions where pedestrians may be difficult to see, while also offering peripheral cues to pedestrians who may be distracted by their phones or other objects rather than the road. Such a system (as one example) that is integrated into a traffic intersection digital twin that tracks all roadway users accurately, has the potential to enhance visibility of vulnerable road users, and thus enhance safety. This paper examines the potential implementation and feasibility of this technology, as well as the safety benefits it could provide. Laboratory experiments with addressable LEDs reveal the capabilities and challenges of this technology for roadway infrastructure safety. These findings could pave the way for more integrated lighting in infrastructure for vehicles and pedestrians at intersections, merge and diverge locations, and other areas where complex interactions present safety hazards. Such lighting solutions, enabled by modern computation and communications, could enhance safety and efficiency in our transportation system and improve overall mobility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Simultaneous Control of Unburned NH 3 and NO x Emissions From High Load Dual-Fuel Ammonia Operation on a High-Speed Diesel Engine Using a Cu-SCR System

Dual-fuel ammonia strategies are being investigated as a promising way to utilize NH 3 as an alternative fuel for internal combustion engines in the maritime sector. One of the remaining barriers to implementing dual-fuel NH 3 combustion strategies is understanding ways to minimize unburned NH 3 and nitrogen oxide (NO x ) emissions from these engines, both of which are elevated relative to a conventional diesel baseline. Selective catalytic reduction (SCR) systems are widely used for lean NO x emission controls for engines across transportation and stationary energy applications. SCR systems use a reducing agent, such as NH 3 , to react with NO x in the exhaust, converting it into nitrogen and water. Typically, NH 3 is injected into the exhaust as a urea solution. In dual-fuel NH 3 engines, where unburned NH 3 is present in the exhaust, an SCR system could be used to mitigate both NH 3 and NO x emissions. The presented work evaluates a commercial copper-zeolite SCR and ammonia slip catalyst system, designed for on-road diesel engine applications, for controlling unburned NH 3 and NO x emissions from a dual-fuel NH 3 combustion engine. The aftertreatment system was installed downstream of a single-cylinder four-stroke diesel engine that has been modified for dual-fuel ammonia use. Furthermore, the emissions were characterized by using a Fourier transform infrared spectrometer for both late- and early-injection diesel pilot strategies over three air–fuel equivalence ratios spanning from 1.6 to 1.0 at 1200 rpm and 12.6 bar IMEP g condition (with greater than 95% ammonia energy fraction). Initial findings indicate that the SCR achieves more than 99% NO x conversion with less than 50 ppm NH 3 slip at air–fuel equivalence ratios greater than 1.4 at the operating conditions investigated. However, these benefits are accompanied by additional N 2 O emissions that are formed over the Cu-SCR.

Catalysts↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗
DOE OSTI2024

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

42 ENGINEERING↗
DOE OSTI2020

Vehicle Platooning: An Energy Consumption Perspective

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. pre-determined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5 %, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A real-time energy and cost efficient vehicle route assignment neural recommender system

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).

Energy consumption↗