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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Quantifying Annual Industrial Locomotive Energy Consumption in the United States

While US Class 1 railroad locomotive rosters and annual fuel consumption are well-documented, considerably less is known regarding the overall energy consumption of operations involving industrial locomotives. To determine the energy savings potential of this rail operating sector, the objective of this research is to develop an inventory of US industrial locomotives and a baseline estimate of their annual energy consumption. Creating an industrial locomotives roster from public data is challenging given their diverse ownership by shippers or leasing companies, and operating locales largely out of public view. By cross-referencing public data on locomotive reporting marks, serial numbers, online images and aerial images, the project team confirmed the age, model and horsepower of over one thousand industrial locomotives. Estimating energy consumption is complicated by the variability in industrial locomotive types and power ratings, and extreme differences in duty cycles and utilization. Given these limitations, using quantified case study examples and adjustments to standard EPA line-haul and switching duty cycles, bounds on the magnitude of annual US industrial locomotive energy consumption were estimated.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Simple Hybrid Electric Vehicle Fuel Consumption Model for Transportation Applications(DOI: 10.5772/intechopen.89055)

This study presents a simple power-based microscopic hybrid electric vehicle (HEV) fuel consumption model for use in microscopic traffic software and various connected and automated vehicle (CAV) applications, including eco-routing and eco-drive systems. While numerous HEV energy consumption models have been developed, these models are complex and require vehicle engine data deeming them difficult to implement and making them nontransferable. The proposed model was developed using empirical data for a 2010 Toyota Prius—the most popular HEV. The model was then extended to other HEVs thus extending the domain of application of the model. The proposed fuel consumption model estimates the instantaneous fuel consumption rates of an HEV using instantaneous vehicle operational input variables, including the vehicle’s speed, acceleration, and roadway grade, which can be acquired from global positioning system (GPS) equipment or other sensors. The model estimates vehicle fuel consumption rates consistent with empirical data producing an average error of 2.1% for the Toyota Prius and up to 4% for other HEVs demonstrating the applicability and transferability of the model to various HEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of Automated Pipeline for Time-Resolved Link-Wise Vehicular Energy Consumption in the Chattanooga, TN Road Network

The Department of Energy (DOE) has shown strong interest in detecting energy inefficiencies in regional road networks, so as to derive energy consumed at a high spatial temporal resolution. We have developed a workflow to automate the estimation of time-resolved vehicular energy consumption over each link in a road network of interest. The road network used in the current work is centered around the city of Chattanooga, Tennessee and its bordering regions. Utilizing the most mature road network for the Chattanooga, TN region, vehicle speed & count data from TomTom in conjunction with machine learning methods, we have developed an automated pipeline to estimate energy consumption for every link in the network. The first step in the pipeline is ingesting vehicle probe counts and speed estimates from TomTom API. In the next step, the probe counts, speed profiles and other exogenous data (i.e. road types, weather data, ground-truth volume counts and more) were used as input to a supervised learning algorithm to estimate the number of vehicles throughout the entire region for each road segment. These volume estimates were then mapped to a unified road network that contained additional important information such as percentage change in gradient across a link, number of lanes and link lengths that are features in pre-trained single vehicle energy-consumption models available with the RouteE software developed at NREL. The per vehicle energy consumption on each road link predicted using appropriate RouteE vehicular models were multiplied by the volume estimate for the corresponding link over a given time period to predict energy consumed per link for the time interval of interest. Currently, work is underway to improve both the RouteE per vehicle energy estimate and the volume estimates derived from TomTom probe counts. We have also explored the correlation of the link-wise energy estimates with the features of the pre-trained RouteE machine learning model in order to gain insight into what factors contribute most to the link-wise energy consumption.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗
DOE OSTI2021

Energy Saving Analysis Using Energy Intensity Usage and Specific Energy Consumption Methods

This study presents the energy saving analysis reached through employing the energy intensity usage and specific energy consumption method. The energy analyses conducted in this study are used for implementing a new technology. However, they are additionally used to evaluate the latest concepts, techniques, processes, and uses for technologies, which are aimed at improvement of energy savings. This study shows the correlation between energy consumption, potential energy savings, and the impact of the energy assessment in different industrial sectors. The correlation is found by using two indicators: (1) the energy intensity usage (EIU) and (2) the specific energy consumption (SEC). The data analysis in this study considers the assessments for 67 industries from 2015 to 2019 and classifies those assessments using the Standard Industrial Classification (SIC) code. The results show that energy savings and energy consumption are linearly related. Also, the energy assessment improves energy performance in a more significant way for smaller companies than for larger industries. Furthermore, these results can be extrapolated by identifying the potential benefits of the energy management system (EMS) implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Video game console usage and US national energy consumption: Results from a field-metering study

There has been an increased in attention placed on the energy consumption of miscellaneous electronic loads in buildings by energy analysts and policymakers in recent years. The share of electricity consumed by consumer electronics in US households has increased in the last decade. Many devices, however, lack robust energy use data, making energy consumption estimates difficult and uncertain. Video game consoles are high-performance machines present in approximately half of all households and can consume a considerable amount of power. The precise usage of game consoles has significant uncertainty, however, leading to a wide range of recent national energy consumption estimates. We present here an analysis based on field-metered usage data, collected as part of a larger field metering study in the USA. This larger study collected data from 880 households in 2012 on a variety of devices, including 113 game consoles (the majority of which are Generation 7 consoles). From our metering, we find that although some consoles are left on nearly 24 h/day, the overall average usage is lower than many other studies have assumed, leading to a US national energy consumption estimate of 7.1 TWh in 2012. Nevertheless, there is an opportunity to reduce energy use with proper game console power management, as a substantial amount of game console usage occurs with the television turned off. The emergence of Generation 8 consoles may increase national energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Fuel consumption impacts of auto roof racks

The after-market roof rack is one of the most common components attached to a vehicle for carrying over-sized items, such as bicycles and skis. It is important to understand these racks' fuel consumption impacts on both individual vehicles and the national fleet because they are widely used. We estimate the national fuel consumption impacts of roof racks using a bottom-up approach. Our model incorporates real-world data and vehicle stock information to enable assessing fuel consumption impacts for several categories of vehicles, rack configurations, and usage conditions. In addition, the model draws on two new data-gathering techniques, on-line forums and crowd-sourcing. The results show that nationwide, roof racks are responsible for 0.8‰ of light duty vehicle fuel consumption in 2015, corresponding to 100 million gallons of gasoline per year. Sensitivity analyses show that results are most sensitive to the fraction of vehicles with installed roof racks but carrying no equipment. Here, the aerodynamic efficiency of typical roof racks can be greatly improved and reduce individual vehicle fuel consumption; however, government policies to minimize extensive driving with empty racks--if successful--could save more fuel nationally.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2017

Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

Consumptive Water Use from Electricity Generation in the Southwest under Alternative Climate, Technology, and Policy Futures

This research assesses climate, technological, and policy impacts on consumptive water use from electricity generation in the Southwest over a planning horizon of nearly a century. We employed an integrated modeling framework taking into account feedbacks between climate change, air temperature and humidity, and consequent power plant water requirements. These direct impacts of climate change on water consumption by 2095 differ with technology improvements, cooling systems, and policy constraints, ranging from a 3–7% increase over scenarios that do not incorporate ambient air impacts. Upon additional factors being changed that alter electricity generation, water consumption increases by up to 8% over the reference scenario by 2095. With high penetration of wet recirculating cooling, consumptive water required for low-carbon electricity generation via fossil fuels will likely exacerbate regional water pressure as droughts become more common and population increases. Adaptation strategies to lower water use include the use of advanced cooling technologies and greater dependence on solar and wind. Water consumption may be reduced by 50% in 2095 from the reference, requiring an increase in dry cooling shares to 35–40%. Alternatively, the same reduction could be achieved through photovoltaic and wind power generation constituting 60% of the grid, consistent with an increase of over 250% in technology learning rates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗
DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Estimating Reduced Consumption for Dynamic Demand Response

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus microgrid, and our preliminary results set the foundation for more detailed modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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 OSTI2026

Validating Simulated Models of Energy Consumption by a Battery Electric Motorcoach: A real-world deployment in a harsh climate.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2015

Development of a Life Cycle Inventory of Water Consumption Associated with the Production of Transportation Fuels

The production of all forms of energy consumes water. To meet increased energy demands, it is essential to quantify the amount of water consumed in the production of different forms of energy. By analyzing the water consumed in different technologies, it is possible to identify areas for improvement in water conservation and reduce water stress in energy-producing regions. The transportation sector is a major consumer of energy in the United States. Because of the relationships between water and energy, the sustainability of transportation is tied to management of water resources. Assessment of water consumption throughout the life cycle of a fuel is necessary to understand its water resource implications. To perform a comparative life cycle assessment of transportation fuels, it is necessary first to develop an inventory of the water consumed in each process in each production supply chain. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model is an analytical tool that can used to estimate the full life-cycle environmental impacts of various transportation fuel pathways from wells to wheels. GREET is currently being expanded to include water consumption as a sustainability metric. The purpose of this report was to document data sources and methodologies to estimate water consumption factors (WCF) for the various transportation fuel pathways in GREET. WCFs reflect the quantity of freshwater directly consumed per unit production for various production processes in GREET. These factors do not include consumption of precipitation or low-quality water (e.g., seawater) and reflect only water that is consumed (i.e., not returned to the source from which it was withdrawn). The data in the report can be combined with GREET to compare the life cycle water consumption for different transportation fuels.

energy-water nexus↗
DOE OSTI2019

PHEV Real World Driving Cycle Energy and Fuel and Consumption Reduction Potential for Connected and Automated Vehicles

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge. After completing 30 baseline drive cycles, a design for six sigma (DFSS) L18 array was designed to look at sensitivity of a range of parameters to energy consumption as related to connected and automated vehicles to target highest return on engineering development effort. The parameters explored in the DFSS array that showed the most sensitivity, in order of importance, were battery state of charge, vehicle mass, propulsion system thermal conditioning, HVAC setting and driver behavior. Each of these areas are explored for energy savings and discussed briefly in the context of CAV control opportunity for energy savings potential.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2002

The effects of chronic alcohol consumption and exercise on the skeleton of adult male rats

BACKGROUND: Lifestyle factors are known to affect skeletal development and integrity. Specifically, running has been reported to increase risk of fatigue fractures, whereas chronic alcohol consumption has been shown to reduce bone formation and bone mass. The combined effect of exercise and alcohol on the skeleton has yet to be explored, although alcohol consumption is common among certain physically active populations (e.g., military recruits, college athletes). It was hypothesized that chronic alcohol consumption would accentuate the inherent risk associated with endurance running exercise. METHODS: Six-month-old male Sprague Dawley rats were assigned to one of five groups: baseline, exercise-alcohol diet, exercise-normal diet, sham-alcohol diet, and sham-normal diet. Alcohol-fed rats (35% caloric intake) received a liquid diet ad libitum. Normal animals were pair-fed the identical diet with a maltose dextrin caloric substitute. Exercise was conducted on a motorized treadmill 5 days/wk for 16 weeks. Sham rats were placed on a stationary treadmill for matching time periods. Fluorochrome labels were administered 3 days before baseline and at 10 and 2 days before animals were killed. Heart, soleus, and rectus femoris muscles were wet weighed to assess the effects of training. Tibiae were collected for static and dynamic histomorphometric measurements on cancellous and cortical bone. RESULTS: Muscle weights were larger in the exercised rats versus the sham rats. Alcohol had no significant effect on skeletal muscle weight but did result in larger heart weights in both alcohol-treated groups. Cancellous and periosteal bone formation rates were significantly decreased in the alcohol-fed rats versus rats on the normal diet and were associated with a significant reduction in trabecular thickness in the tibial metaphysis. Cortical and cross-sectional areas were also significantly lower in the alcohol-fed groups compared with the non-alcohol-fed groups. Exercise had no significant effect on cancellous or cortical bone measurements. CONCLUSIONS: Chronic alcohol consumption significantly reduced bone formation. Exercise had no effect on the bone and did not attenuate any of the negative effects of alcohol. The results suggest that alcohol consumption weakens the skeleton and increases the incidence of endurance-exercise-related bone injuries. Thus, individuals who are participating in endurance exercise and consuming alcohol may be at greater risk for exercise-related skeletal injuries. Further investigation of the potential for alcohol to induce detrimental effects on the hearts of individuals participating in endurance exercise is indicated.

NASA Discipline Cell Biology↗
DOE OSTI2017

Pittsburgh 2013 Energy Baseline: Consumption, Trends & Opportunities

This study examines energy consumption patterns within the Pittsburgh metropolitan area to establish a baseline for energy consumption and associated air emissions. Monthly electricity and natural gas consumption for the residential, commercial, and industrial sectors is reported at the zip code-level, utilizing 2013 utility data (the last complete year available). Electricity load profiles for the Duquesne Light service territory are also reported, examining hourly, daily, and monthly load trends. These data were then utilized to identify trends in consumption, emissions associated with different sectors and fuel types, and potential opportunities for improvement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Resource demand growth and sustainability due to increased world consumption

The paper aims at continuing the discussion on sustainability and attempts to forecast the impossibility of the expanding consumption worldwide due to the planet’s limited resources. As the population of China, India and other developing countries continue to increase, they would also require more natural and financial resources to sustain their growth. We coarsely estimate the volumes of these resources (energy, food, freshwater) and the gross domestic product (GDP) that would need to be achieved to bring the population of India and China to the current levels of consumption in the United States. We also provide estimations for potentially needed immediate growth of the world resource consumption to meet this equality requirement. Given the tight historical correlation between GDP and energy consumption, the needed increase of GDP per capita in the developing world to the levels of the U.S. would deplete explored fossil fuel reserves in less than two decades. These estimates predict that the world economy would need to find a development model where growth would be achieved without heavy dependence on fossil fuels.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗