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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of Economic Viability of Direct Current Fast Charging Infrastructure Investments for Electric Vehicles in the United States

As the global transportation sector increasingly adopts electric vehicles, the demand for advanced and accessible charging infrastructure is rising. In addition to at-home electric vehicle (EV) charging, there is a growing need for the swift development of commercial direct current fast charging (DCFC) stations to meet on-the-go EV charging demands. While government funds are available to support the expansion of the EV charging network in the United States, the establishment of a robust nationwide EV charging infrastructure requires significant private sector investment. This study was conducted to assess the economic feasibility of various business models for fast charging stations in the U.S. using two case studies and exploring different operational strategies including sole ownership and collaborative ventures with public and private entities. The results indicate that based on the current adoption and utilization rates in the U.S., the business model involving an owner-operator collaborating with a public partner ensures profitability and protects the investment in DCFC stations from financial losses. The study also highlights that demand charges and electricity retail prices are the factors that affect the profitability of a DCFC station.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Integrating Electric Vehicles into the Grid

Historically, transportation and power systems operated independently, but the rise of electric vehicles is transforming this relationship. In the United States, EV demands are expected to become the largest source of electricity load growth, posing challenges for the grid if not properly managed. This presentation highlights how managed charging strategies and strategic infrastructure deployment can integrate EVs into the power system to enhance grid efficiency and support renewable energy.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗
DOE OSTI2020

LIGHT DUTY VEHICLE CHOICE MODELS EXAMINING ALTERNATIVE FUEL TECHNOLOGY PREFERENCES AMONG COMMERCIAL FLEET OWNERS

Agent-based transportation models are critical tool for evaluating energy use, emissions and mobility impacts of transportation. In this context, vehicle ownership models are critical for linking agent activities to their impacts on the transportation system. However, no agent-based freight transportation models to date has been developed that predicts the fuel technology that is preferred by establishments when purchasing a Light Duty Vehicle (LDV) for business purposes. This research addresses this gap by using recently collected survey data to estimate a discrete choice model that can be applied in the agent-based context.

commercial↗
DOE OSTI2023

A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles

The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. In conclusion, the proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.

42 ENGINEERING↗
DOE OSTI2021

The Transportation Energy and Mobility Pathway Options (TEMPO) Model: Overview and Validation of V1.0 [Slides]

This presentation documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗
DOE OSTI2021

Material transfers detection with seismic observations

We are exploring the use of data from a seismic network around a research nuclear reactor and isotope production facility at Oak Ridge National Laboratory to study activity patterns related to the transfer of nuclear material. A sensor network with eight seismometers was installed around the High Flux Isotope Reactor and the Radiochemical Engineering Development Center and started operating in October 2019. These data are used to detect and characterize operational events around facility. We are using those data to extract signals related to the movement of vehicles involved in the transport of nuclear materials (e.g., transportation of reactor fuel, targets, and product isotopes). Large vehicles produce mechanical energy that can be observed as seismic signals, either as the result of sound emanating from the vehicle engine or by the generation of surface waves as a response of the ground to the load of the vehicles. In particular, the station located near the entrance gate of the facility displays a clear seismic signature as vehicles cross a metal platform on the ground. This signal is characterized by sharp energy bursts that correspond to the number of axles on the vehicle. We developed and are testing an algorithm to identify sequences of energy bursts to count and characterize vehicles of different sizes. We are also exploring the use of seismic polarization analysis to measure the degree of polarization of the observed signals. These two techniques for detection and characterization of vehicles used for nuclear material transfers will be validated with seismic data from a targeted collection using different vehicles and routes similar to the ones used in real-life scenarios at the High Flux Isotope Reactor.

Marcillo, Omar↗
DOE OSTI2026

Fleet Hydrogen Basics

Hydrogen is one transportation fuel option public and private heavy-duty vehicle fleets can consider. Heavy-duty hydrogen fuel cell electric vehicles provide the performance benefits of electric drivetrains (e.g., high instantaneous torque for heavy loads and performance improvements at high altitudes and on steep grades) and can contribute to air quality improvements in high-traffic areas. For some regions and duty cycles, hydrogen could be a better fit for heavy-duty fleets than current battery-electric options because of the increased range and faster fueling times.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Challenges and Opportunities in Decarbonizing the U.S. Energy System

The United States has pledged to develop a 100% carbon-free electric power system by 2035 and a net-zero-emissions economy by 2050. While important advancements have been made in the scale, performance, and economics of clean energy technologies, meeting the nation's ambitious goals will not only require their deployment at scale, but also additional innovation and effective integration of different solutions. Technological developments across the broad suite of low-carbon energy solutions are advancing rapidly, with ongoing innovations in renewable electricity generation, industrial processes, and energy-saving technologies and services, including LED lighting, induction heating, electric vehicles, energy storage solutions, and mobility as a service, plus smart devices, controls, and more efficient and smart buildings. Combining renewable electricity with biotic and abiotic pathways to produce chemicals, fuels, and materials promises to deliver new solutions. Grid-interactive buildings and communities, integrating transportation infrastructure and vehicles, are likely to be significant components of any zero-carbon energy strategy. Low-carbon industrial manufacturing will also make strong contributions to a net-zero economy. While the technical prospects appear promising, variations in the state of infrastructure, jurisdictional and social equity, pollution, economic and socio-cultural constraints, energy resource availability, and supply chain dynamics found in different locations present a range of challenges and demand customized solutions. This paper provides a critical review and offers new insights into the technical, infrastructure, analytic, political, and economic challenges faced in translating the nation's ambitious net-zero-emissions goals into feasible and reliable implementation action plans.

circular economy↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

An integrated transportation-power system model for a decarbonizing world

Rising demand for electricity from electric vehicles (EVs) will require new paradigms to guarantee reliable and low-cost electricity. This study couples an agent-based travel demand simulator and an electricity grid model to assess the economic costs of supplying power to meet EVs' added demand across the Chicago region. Results suggest that shifting from personal EVs to a fleet of shared, fully-automated all-electric vehicles (SAEVs) could lower per-mile emissions, congestion, and embodied vehicle and charging infrastructure emissions. Further, the results should compel policymakers to shift the cost of providing power onto commercial customers, like electric ride-hail fleets, through price-indexed electricity prices, which can shift charging to off-peak periods or away from resource-scarce hours.

Integrated modeling↗
DOE OSTI2020

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2020 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2021

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2021 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2022

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2022 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2023

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2023 Excel)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET is developed as a multidimensional spreadsheet model in Microsoft Excel. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. It includes two sub-models named Fuel-Cycle Model (GREET 1, contains data on fuel cycles and vehicle operations) and Vehicle-Cycle Model (GREET 2, evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2023

Equitable Employment Access Assessed Through the Mobility Energy Productivity (MEP) Metric

This paper examines commuting options for an underserved neighborhood in Columbus, Ohio to a major employment center. The analysis is based on an emerging metric called the Mobility Energy Productivity (MEP) metric developed by the National Renewable Energy Laboratory (NREL) on behalf the Department of Energy (DOE). The purpose of the analysis is twofold. The first is to quantify relative attractiveness of commute modes between the two locations, using a perspective that includes travel time, energy and cost, while providing an equity lens to compare commute options between privately owned vehicles and pooled transportation options. The second objective is to apply MEP in a specific origin-destination (O-D) scenario, whereas previously it has been used primarily as an aggregate metropolitan-wide statistical measure. In so doing, parameters in MEP are further customized and the methodology is refined to account for unique aspects of this case study. Four commute options between the neighborhood and the industry employment based are analyzed: drive alone option, public transit express bus (historical), public transit normal route (current), and a proposed shuttle specific to the O-D pair. This analysis identified issues applying MEP that required further customization: (1) deprecation functions customized to modes other than driving, (2) accounting for first-mile last-mile travel times with transit, (3) accounting for transit frequency without resorting to full simulation. The results provide quantitative insights on the employment accessibility between these two locations, both across modes, and as equity of job accessibility for those who can and cannot operate a personal vehicle.

ADVANCED PROPULSION SYSTEMS↗