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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 OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

LogPath: Log data based energy consumption analysis enabling electric vehicle path optimization

Vehicle navigation and path optimization require a more meticulous approach when it deals with EVs (electric vehicles) and SDVs (software-defined vehicles), due to lengthy charging times and the lack of charging infrastructure. Long-distance freight EV trucking needs path guidance with accurate energy consumption estimates to prevent charging-related failures. We developed a novel energy consumption estimation approach that only uses battery log data to extract major vehicle parameters to increase EV navigation accuracy without additional sensors. This is enabled by extracting multiple drive modes from the log data for analysis. The system provides 1) routes, 2) charge locations, 3) charging times, and 4) optimal vehicle speeds that guarantee the shortest travel time. Here we successfully validated the system using log data collected from an EV and Tesla's Supercharging map in the US and compared it with the commercially available navigation system, Tesla's trip planner, whose capabilities solely include charging time and routing.

EV (Electric vehicles) navigation↗
DOE OSTI2025

Tunneling Barrier-Integrated Gold Nanofilms for Negative Strain Gauging with Near-Zero Energy Consumption

Wireless strain sensors with minimal power needs are essential for long-term monitoring in energy-limited environments. We present a soft tunneling barrier-integrated gold thin film for negative strain sensing with near-zero energy consumption. The device features a strain-induced transition from an insulating to a metallic state, increasing conductivity by 9 orders of magnitude under a controlled strain. It consists of Au-PDMS-Au nanofilm layers, where the Au structures are near the percolation threshold and the PDMS layer acts as a tunneling barrier. Under strain, thinning due to the Poisson effect lowers the barrier’s potential height, enabling electron tunneling and forming an electrical path. Further, with a standby power consumption of ~10 –5 mW over 10 6 times lower than conventional sensors (~12.5 mW), this device is ideal for real-time, long-term stationary structural monitoring in multiple locations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Investigation of Reactive Silver Ink Formula for Reduced Silver Consumption in Silicon Heterojunction Metallization

Silver is the most expensive non-silicon component in photovoltaic cells. This is particularly salient for silicon heterojunction (SHJ) cells, which rely on large quantities of low-temperature silver pastes (LT-SP). SHJ cells would benefit greatly from an industrially scalable metallization process that simultaneously offers low silver consumption, low finger resistivities (<20 μΩ·cm), and low processing temperatures. Printed reactive silver inks (RSI) are an innovative candidate to this end. Furthermore, this work furthers the research on RSI metallization by investigating the impact of ink formula on properties pertinent to SHJ cells, including electrical properties, line width, ink splatter, silver consumption, cell performance, and adhesion. We introduce a scalable, high-throughput flexible needle contact printing approach for metallization that solves many of the issues associated with drop-on-demand printing. The printed silver fingers are characterized using electrical measurements and top-down and cross-sectional microscopy. The best performing ink, consisting of silver acetate, ethylamine, and formic acid, achieved silver fingers with total resistivities of 3.1 μΩ·cm and contact resistivities of 3.2 mΩ·cm 2 when printed at 61 °C. This ink metallized a full-sized 156 mm × 156 mm SHJ cell. This is the first reported data for RSI metallization of a full-sized SHJ cell and shows how an optimized RSI can achieve similar performances to LT-SP while consuming 80–90% less silver.

14 SOLAR ENERGY↗
DOE OSTI2025

Estimating energy consumption and GHG emissions in the U.S. food supply chain for net-zero

This work provides a database of the U.S. food system’s energy consumption and GHG emissions at the national and state levels by food supply chain (FSC) stage, fuel type, and food commodity. We estimate that the U.S. FSC consumed a total 4660 TBTU (4900 PJ) of site energy, 7130 TBTU (7500 PJ) of primary energy, and generated 970 MMT of GHG emissions in 2016. Among all the stages, on-farm production is the largest energy consumer (31% primary energy) and GHG emissions contributor (70%), largely due to raising animals. Optimizing distribution can reduce the stage’s energy consumption and GHG emissions and increase products’ shelf-life. Reducing food loss and waste is another good option, as it decreases the amount of food necessary to grow, thus impacting the overall FSC. The database can help stakeholders identify stage- and region-specific strategies and measures to curtail the environmental footprint of the U.S. food system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Electricity Produced from Photovoltaic Systems in Apartment Buildings and Self-Consumption: Comparison of the Situation in Various IEA PVPS Countries

Over the last two decades, grid-connected solar photovoltaic systems have increased from a niche market to one of the leading power generation capacity additions annually. In 2018 the total installed photovoltaic electricity generation capacity exceeded 500 GW. Another doubling is forecasted until the early 2020s. Therefore, the further development is coupled with the question at what prices solar photovoltaic electricity can be provided and delivered to the customers. The installation of PV systems for self-consumption is already now an interesting option for many people but in general limited to those who have access to a rooftop they own or can use. A new envelopment to enable residents of multi apartment buildings to commonly use electricity generated by a PV system (collective self-consumption) is a relatively new development and is still facing a lot of administrative and regulatory challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Modeling the External Effects of Air Taxis in Reducing the Energy Consumption of Road Traffic

Air taxis are currently being demonstrated. Few studies have quantified their external effects in reducing on-road vehicle fuel consumption. The hypothesis of this paper is that air taxis may divert some drivers away from congested traffic corridors, improve traffic speed and fuel economy, and reduce congestion-induced energy consumption. A model is developed that links several key components: mode choice, the relationship between travel demand and traffic speeds, the relationship between traffic speeds and fuel economies, and the heterogenous value of travel time. It is applied to the route from downtown Los Angeles to Los Angeles International Airport, where at peak hours 38,200 vehicles attempt to use the route that has an hourly capacity of 17,200 vehicles. The model estimates that, with conservative assumptions and near-term technologies, diverting 3.2% of the traffic to air taxis could produce a 15% reduction in traffic vehicle fuel use. With optimistic assumptions and mature technologies, the study estimates that diverting 20% of traffic could reduce the traffic vehicle fuel use by about 74%. The key insight is that if a small share of congested travelers switched to air taxis, motivated by private benefits of time savings, significant external benefits for other road travelers (time savings and fuel savings) and to society (reduced energy use and emissions), would ensue creating a win-win-win outcome. Overall, these estimates (which are not intended as predictions because of the stated limitations) strongly suggest the need to consider the external energy effect in future cost-benefit analyses of air taxi technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Canadian Oil Sands Extraction and Upgrading: A Synthesis of the Data on Energy Consumption, CO 2 Emissions, and Supply Costs

As Canadian crude bitumen production from oil sands has increased in recent decades, the nation’s oil and gas industry has become a significant contributor to national greenhouse gas emissions. Canada has developed carbon emission reduction targets to meet its Nationally Determined Contributions and Mid-Century Strategy goals. A detailed profile of energy consumption pathways in the oil sands industry is necessary to identify potential areas of improvement and to monitor progress toward meeting emissions reduction targets. Much of the existing literature for oil sands modeling provides input assumptions with different technological boundaries. For a set of oil sands extraction and upgrading technologies, this study first reviews the literature and then quantifies energy input requirements, CO 2 emissions, and operating costs for a set of consistent technological boundaries and energy units. Summary results refer to requirements and costs at the production facility, excluding transportation and blending costs. An energy system diagram of oil sands production that matches these boundaries is provided, which can be used by integrated assessment models, oil sands companies, and government ministries to evaluate the present and future energy consumption and emissions pathways of the oil sands industry.

02 PETROLEUM↗
DOE OSTI2021

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗
DOE OSTI2024

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse buildings can reduce model performance. The impact of increasing dataset heterogeneity in time series forecasting, while keeping size and model constant, is understudied. We tackle this issue using the ComStock dataset, which provides synthetic energy consumption data for U.S. commercial buildings. Two curated subsets, identical in size and region but differing in building type diversity, are used to assess the performance of various time series forecasting models, including finetuned open-source foundation models (FMs). The results show that dataset heterogeneity and model architecture have a greater impact on post-training forecasting performance than the parameter count. Moreover, despite the higher computational cost, finetuned FMs demonstrate competitive performance compared to base models trained from scratch.

commercial buildings↗
DOE OSTI2023

Oxygen consumption of sexually mature adult, first-feeding larval, and yearling Pacific Lampreys

Abstract Objective The Pacific Lamprey Entosphenus tridentatus is considered a first food of Native American tribes, such as the Confederated Tribes of the Umatilla Indian Reservation. Populations of this imperiled species have declined such that harvest for traditional use is limited. As a result, propagation and culture of Pacific Lampreys have been initiated to supply animals for restoration and recovery efforts. These efforts require transport and periodic holding of both adults and larvae under static (no-flow) conditions. Hence, guidelines for ensuring an adequate oxygen supply are needed for all life stages of this species. Methods We measured oxygen consumption rates of mature adults, first-feeding larvae, and yearlings under static conditions at 12–15°C. In addition, we recorded indicators of stress during hypoxia for adults and estimated routine respiration rates during and after larval feeding. Result Adults exhibited surprisingly high metabolic rates when at rest in static chambers. At 12°C, a single adult typically reduced dissolved oxygen levels in 10 L to <2 mg/L in just 90 min (oxygen consumption rates of 100–200 mg/kg/h). Adults often started to climb the walls of open chambers when dissolved oxygen levels approached 2 mg/L, allowing them to raise their branchiopores into air. Larvae remained quiescent, even when oxygen levels dropped below 1 mg/L, and costs of feeding increased routine respiration by 22–56%. Conclusion Based on these observations, we recommend that adult Pacific Lampreys always be transported in aerated containers and with access to air at the top of the tank. Although larvae exhibited hypoxia tolerance, care should be taken to ensure adequate oxygen availability, particularly during and immediately after feeding.

Moser, Mary↗
DOE OSTI2020

Automotive fuel cell stack and system efficiency and fuel consumption based on vehicle testing on a chassis dynamometer at minus 18 °C to positive 35 °C temperatures

In this work we present an in-depth laboratory technology assessment of a 2016 Toyota Mirai Fuel Cell (FC) vehicle based on chassis dynamometer testing. The 114.6 kW FC stack has a high dynamic response, which makes this powertrain a FC-dominant hybrid electric vehicle. The measured peak efficiency is 66.0% FC stack and 63.7% FC system with an idle hydrogen flow rate of 4.39 g/hr. The high FC system efficiencies at low loads match typical vehicle power spectrums, resulting in a high average vehicle efficiency of 62% compared to 45% and 23% for a hybrid electric vehicle and a conventional vehicle, respectively. An energy breakdown accounts for the FC stack losses, FC system losses, air compressor loads, and heater loads for different drive cycles and different thermal conditions. The cold-start North American city drive cycle (UDDS) energy consumption values are, respectively, 758, 581, 226, and 321 Wh/km at ambient conditions of -18 degrees C, -7 degrees C, -25 degrees C and 35 degrees C with 850 W/m 2 of solar loading. The FC system shutdown and startup processes at temperatures below the freezing point contribute to the increased hydrogen consumption. Additionally, the raw test data files are available for download, thus providing the research community with a public reference data on a modern production automotive FC system.

08 HYDROGEN↗
DOE OSTI2024

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Abstract Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development.

Environmental Sciences & Ecology↗
DOE OSTI2026

Cold compressor performance and energy consumption improvements at Jefferson Lab’s Central Helium Liquefiers

Abstract Jefferson Lab operates two central helium liquefiers (CHLs) which both utilize full cold compression from the saturation pressure at operating temperature (approximately 2.1 K) to just over atmospheric pressure. The original plant, CHL1, was recently outfitted with a replacement subatmospheric cold box (SC1R) containing state-of-the-art cold compressor technology, while the newer plant, CHL2, uses an older cold compressor system. In both cases, the heat of compression is absorbed at low temperature at the expense of electrical power consumed by the warm compressors. Due to the superior efficiency and turndown capabilities of SC1R, a new operating mode has been identified for CHL1 in which the required number of operating warm compressors is reduced by one. A cost-based method for optimizing cold compressor stability and efficiency has been developed and applied to CHL2, improving its turndown and lowering the warm compressor discharge pressure. As a result of these efforts, power consumption of the combined CHLs during normal operations has been reduced by nearly 10%, or a total of 650 kW. The observed performance of CHL1 with SC1R, as well as the cold compressor optimization method, leading to this improved energy consumption rate will be discussed in detail.

Mastracci, B [Thomas Jefferson National Accelerato↗
DOE OSTI2020

Incubation Temperature Affects Duckling Body Size and Food Consumption Despite No Effect on Associated Feeding Behaviors

Synopsis Developmental conditions can have consequences for offspring fitness. For example, small changes (<1°C) in average avian incubation temperature have large effects on important post-hatch offspring phenotypes, including growth rate, thermoregulation, and behavior. Furthermore, average incubation temperatures differ among eggs within the same nest, to the extent (i.e., >1°C) that differences in offspring phenotypes within broods should result. A potential consequence of within-nest incubation temperature variation is inequality in behaviors that could cause differences in resource acquisition within broods. To investigate this, we incubated wood duck (Aix sponsa) eggs at one of two ecologically-relevant incubation temperatures (35°C or 36°C), formed mixed-incubation temperature broods after ducklings hatched, and conducted trials to measure duckling behaviors associated with acquisition of heat (one trial) or food (three trials). Contrary to our predictions, we found no effect of incubation temperature on duckling behaviors (e.g., time spent occupying heat source, frequency of feeding bouts). However, we found evidence that ducklings incubated at the higher temperature consumed more food during the 1-h feeding trials, and grew faster in body mass and structural size (culmen and tarsus) throughout the study, than those incubated at the lower temperature. Apparent food consumption during the trials was positively related to culmen length, suggesting that differences in food consumption may be driven by structural size. This could result in positive feedback, which would amplify size differences between offspring incubated at different temperatures. Thus, our study identifies incubation temperature as a mechanism by which fitness-related phenotypic differences can be generated and even amplified within avian broods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Wheat respiratory O2 consumption falls with night warming alongside greater respiratory CO2 loss and reduced biomass

Abstract Warming nights are correlated with declining wheat growth and yield. As a key determinant of plant biomass, respiration consumes O2 as it produces ATP and releases CO2 and is typically reduced under warming to maintain metabolic efficiency. We compared the response of respiratory O2 and CO2 flux to multiple night and day warming treatments in wheat leaves and roots, using one commercial (Mace) and one breeding cultivar grown in controlled environments. We also examined the effect of night warming and a day heatwave on the capacity of the ATP-uncoupled alternative oxidase (AOX) pathway. Under warm nights, plant biomass fell, respiratory CO2 release measured at a common temperature was unchanged (indicating higher rates of CO2 release at prevailing growth temperature), respiratory O2 consumption at a common temperature declined, and AOX pathway capacity increased. The uncoupling of CO2 and O2 exchange and enhanced AOX pathway capacity suggest a reduction in plant energy demand under warm nights (lower O2 consumption), alongside higher rates of CO2 release under prevailing growth temperature (due to a lack of down-regulation of respiratory CO2 release). Less efficient ATP synthesis, teamed with sustained CO2 flux, could thus be driving observed biomass declines under warm nights.

Posch, Bradley C. (ORCID:0000000309246608)↗
DOE OSTI2022

Load Disaggregation (Modeling Individual Appliance Power Generation & Consumption in Real-Time)

We developed a machine learning-based load disaggregation method to estimate the real-time output of individual appliances from the whole-house measurements. We first learn the important features associated with each type of appliances using the ground truth consumption data of individual appliances potentially available for a small set of houses equipped with submeters. The learned features are then be used to estimate the power generation/consumption of the appliances from the whole-house consumption. This developed load disaggregation software includes two steps. The first step is to identify the on/off status of different appliances using a classification method, and the second step is to estimate the appliance output using a regression method.

Hao, Jun↗
DOE OSTI2020

Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

33 ADVANCED PROPULSION SYSTEMS↗