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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integration of a Smart Outlet-Based Plug Load Management System with a Building Automation System

The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.

building automation system↗
DOE OSTI2022

Frequency Restoration of Grid-Forming Inverters in Pulse Load and Plug-in Events

This article presents a restoration technique using grid-forming inverters in an islanded microgrid during pulse load and plug-in events. In a microgrid powered by droop-controlled inverters, frequency is the variable that is accessible to all inverters for adjusting their power contributions. The problem is that the microgrid frequency deviates from its nominal value after a load change. The presented method enables the inverters to restore the frequency to the nominal value and regulate their bus voltage amplitudes. The frequency and voltage restorations are performed without communication while achieving the desired power-sharing between grid-forming inverters. The restoration is activated after detecting any active or reactive power changes. In this article, the dynamic model of an inverter equipped with enabled restoration paths is developed to verify the stability of the inverter controller under various conditions. The frequency and voltage restorations are examined during pulse load and plug-in events. Furthermore, this study is performed using a laboratory-scale islanded microgrid powered by two 208 V, 60 Hz, 5 kVA inverters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Managing Army Plug Load Equipment Energy Use: Office Equipment

Office equipment covers a large and diverse group of plug load devices. Policies focused on equipment purchase, power management, and usage limitations can save the Army more than 10 million kWh and $620K per year within just four building categories. This represents a top opportunity for energy- and cost-savings identified by a recent Army plug load study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Study Highlights: Characterizing Plug Load Energy Use and Savings Potential in Army Buildings

A recent study of plug load devices in Army buildings has identified opportunities to save over 83 million kWh of electricity valued at over $5 million per year. These savings may be conservative and were identified by inventorying and monitoring plug load and miscellaneous electric load (MEL) equipment within representative Army buildings. The objective was to better understand the energy consumed by these devices and identify approaches for reducing it while enhancing resilience. This summary highlights the findings, lessons learned, and recommendations identified by the study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Refining Control, Charging, and Battery Chemistry for CO 2 e Savings in Heavy-Duty Off-Road Plug-In Series Hybrid

With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO 2 e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO 2 e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO 2 e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. Furthermore, these strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO 2 e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO 2 e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO 2 e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Plug Load Management Through Occupant Engagement at Washington State University

To decrease costs and improve building occupant safety, Washington State University (WSU) began planning a plug load management campaign in 2020, with implementation beginning in 2021. This case study describes the campaign's goals, approach, and results over the past 5 years, ultimately showing that occupant- and behavior-focused strategies can meaningfully reduce plug load energy use while improving health and safety.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Heat activates the AAA+ HslUV protease by melting an axial autoinhibitory plug

At low temperatures, protein degradation by the AAA+ HslUV protease is very slow. New crystal structures reveal that residues in the intermediate domain of the HslU 6 unfoldase can plug its axial channel, blocking productive substrate binding and subsequent unfolding, translocation, and degradation by the HslV 12 peptidase. Biochemical experiments with wild-type and mutant enzymes support a model in which heat-induced melting of this autoinhibitory plug activates HslUV proteolysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nanoscopic Plugs Block Hydrogen Crossover in Submicron Thick Proton-Conducting SiO 2 Membranes for Water Electrolysis

Zero-gap electrolyzers based on submicron thick proton-conducting oxide membranes (POMs) represent a promising approach to increasing the efficiency of H 2 production from water electrolysis while moving away from conventional perfluorosulfonic acid (PFSA) membranes. A critical barrier to the commercialization of such electrolyzers is that the ultrathin nature of POMs, which is necessary to achieve low cell resistance, makes them more susceptible to defects that can lead to unacceptably high rates of H 2 crossover. Herein, we demonstrate an approach to mitigate this problem through selective deposition of carbon-containing silicon oxide (SiO x C y ) “nanoplugs” into the defects of submicron thick SiO 2 membranes using a facile electrochemically mediated deposition process. Selective deposition of nanoplugs within the defects was verified by multiple characterization techniques, while scanning electrochemical microscopy (SECM) was used to confirm selective plugging of H 2 -crossover hotspots associated with defects at identical locations. Thanks to the use of nanoplugs, the H 2 permeance of 250 nm thick SiO 2 membranes was reduced by 5 to 6 orders of magnitude compared to the unmodified atomic layer deposition (ALD) SiO 2 membranes while having negligible impact on the ionic resistance of the membrane. These plug-modified membranes also enabled safe and stable operation of a zero-gap full cell electrolysis cell, in contrast to cells lacking nanoplugs that produced anode effluent streams having H 2 concentrations near or exceeding the lower flammability limit (LFL) of H 2 . Furthermore, beyond water electrolysis, this defect-sealing strategy has the potential to be broadly implemented in other applications, such as fuel cells and flow batteries, offering a versatile solution to mitigate crossover-related performance losses.

ALD SiO2↗
DOE OSTI2021

Standard Modular Architecture for Consumer End Plug and Play Interfaces

The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.

Chinthavali, Madhu Sudhan↗
DOE OSTI2020

Development of Control System Functional Capabilities within the IES Plug-and-Play Simulation Environment

The concept of an Integrated Energy System (IES) is meant to combine different energy technologies in synergistic ways to achieve a more secure and economical energy supply. The RAVEN-based HYBRID framework is used to find the optimal installed capacity and the optimal economical dispatch of each component of the IES. A new RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization (HERON) has been developed for optimizing the production variables of the IES given the demand profile. Currently, only the limits that affect the production variables and their corresponding time rates of change are considered (explicit constraints). However, other variables are additionally subject to constraints, but the associated limits are not accounted for (implicit constraints). In particular, for the power dispatch problem, the optimization algorithm takes into account the limits on the electrical power output and the corresponding hourly power variations but does not consider other constraints on process variables whose response effects the service life of the IES. This report describes a scheme that allows accounting for implicit constraints without increasing the size of the optimization problem. The Reference Governor (RG) algorithm is traditionally used for enforcing state and control constraints by modifying the set-point trajectories supplied to the feedback regulators. In our application, the RG is coupled within an iterative loop with the HERON-power dispatcher to generate optimal trajectories that ensure the operational constraints are met. A data-driven procedure to derive a representation of the dynamics of the controlled system was developed. First, the variables that represented the state of the system are selected (PCA-based approach), and then state-space representation matrices are derived from the collected measurements (DMDc algorithm). A preliminary version of the developed workflow based on Linear Time Invariant matrices was assessed by adopting a two-unit test case. More sophisticated versions of this workflow foreseeing the on-line derivation of system matrices will be deployed in FY 2021. Finally, a “plug-and-play” library of controllers and state observers was developed in Dymola. Some aspects of the current configuration of the IES unit components, e.g., the encapsulation of the control schemes into dedicated blocks, are consistent with the “plug-and-play” philosophy. Other features, e.g., the system buses collecting the input and the output variables, are not. For this reason, once listed and described the limits of the current configuration, necessary modifications to the plant model interface are presented. As a test-case, the interfaces of the SES model in the RAVEN-based HYBRID framework were reworked accordingly, and two different control schemes were applied to the same plant model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2021

The number of plug-in electric vehicles (PEVs) sold in the United States has consistently grown since 2010, reaching 4% of the light-duty vehicle market in 2021. This report examines how the characteristics for these PEVs has changed over this decade, evaluating range, energy efficiency, costs, and performance. Given the vehicle characteristics, this report estimates miles driven, electricity consumption, petroleum reduction, and greenhouse gas emissions attributable to electric vehicles. This report also explores vehicle manufacturing and battery production, considering supply chains from battery cells to assembly. Over 2.1 million PEVs have been sold in the United States through December 2021, with 1.3 million of these all fully-electric battery electric vehicles (BEV), and 800,000 plug-in hybrid electric vehicles (PHEV) which have the capability of using gasoline. The sales-weighted average range for BEVs reached 290 miles in 2021 and 28 miles for PHEVs. We estimate that electric vehicles have driven 68 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.54% in 2021 and 2.5 billion gallons cumulatively through 2021. In 2021, PEVs used 6.1 terawatt-hours of electricity to drive 19.1 billion miles, offsetting 700 million gallons of gasoline. We find that this fuel switching reduced consumer fuel costs by $1.3 billion in 2021. Since 2010, 65% of PEVs sold in the United States have been assembled domestically, and over 110 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

25 ENERGY STORAGE↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Adoption of Plug-in Electric Vehicles: Local Fuel Use and Greenhouse Gas Emissions Reductions Across the U.S.

The dependence on gasoline-powered light-duty automobiles has made U.S. households vulnerable to the burden of fuel costs. Tailpipe emissions from these vehicles constitute 58% of greenhouse gas (GHG) emissions in the U.S., which are damaging to the environment (EPA, 2023). The adoption of plug-in electric vehicles (PEVs) has been shown to effectively reduce fuel costs and GHG emissions. However, local effects on these benefits are not well understood by American consumers, potentially limiting adoption and therefore the realization of PEV benefits at scale (MacInnis & Krosnick, 2020; EY Americas, 2023). To fill this research gap, this study estimates the fuel cost savings and GHG emission reductions at the state and ZIP code levels by considering local fuel prices, vehicle class preference, average vehicle model year, fuel efficiencies, and driving intensities. The study's findings reveal that the adoption of PEVs can yield substantial benefits in terms of fuel cost savings and GHG emission reductions nationwide. Specifically, driving a battery electric vehicle (BEV) is estimated to result in annual savings of up to $\$2,200$, while driving a plug-in hybrid electric vehicle (PHEV) can lead to savings up to $\$1,500$, when compared to an internal combustion engine vehicle (ICEV) of equivalent size. Moreover, using population-weighted averages by ZIP code, BEVs and PHEVs show the potential to save 400 and 200 grams of carbon dioxide equivalent per mile, respectively, compared to a representative ICEV of the same class. The magnitude of fuel cost savings and emissions reduction vary by region due to various factors. Generally, regions with high gasoline prices, low electricity prices, preferences for larger vehicles, and high driving intensities tend to see relatively large fuel savings. The emissions reductions are more pronounced in areas with clean grids where consumer preferences lie with large vehicles. This regional variability underscores the importance of considering local contextual factors when assessing the potential benefits of PEV adoption. In more than 99% of U.S. ZIP codes, PEVs result in overall savings in fuel use (and subsequent costs) and GHG emissions. While not a central focus of this analysis, reductions in GHG tailpipe emissions from PEV adoption would also come with reductions in criteria pollutant emissions, contributing to improved local air quality depending on the PEV penetration, population density, and electricity generation infrastructure in the locality.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Comparison of plug flow and multi-node stratified tank modeling approaches regarding computational efficiency and accuracy

Residential water heaters contain water stratified by temperature-driven density differences. This implies that a water tank can reach a state in which the top and bottom sections have different temperatures, unless mixing happens. A high degree of thermal stratification can improve the efficiency of some water heaters, by saving the amount of energy required for the heat-up process. Studies of stratification became popular in the 1970s and it remains an active research topic today. The research has led to the development of different models and techniques to better predict and define a stratified tanks behavior. By comparing these models and techniques used previously to describe thermal stratification, the phenomenon could be better understood, exploited, and used to increase efficiency and thermal energy capacity in modern water tanks. From the existing models, we found the one-dimensional standard plug-flow and a multi node model to be appropriate for analyzing the processes of the heat up and cool-down in a water tank. These two models are based on energy balances. This work involved comparing the accuracy and computational effort needed to implement these models. To assess accuracy, we compared both types of existing models to experimental data (also collected in this work) which included a heat up process using an external heat pump. This external process included a layering process that has an eddy diffusivity at five times the rate of thermal diffusion. For this project, we implemented the models in MATLAB, the multi-paradigm numerical computing environment. We quantified model accuracy using the root mean squared error between modeled data and experimental data for six measured tank temperatures. Comparing the accuracy and the computational time taken to run the simulation provides a method to contrast the performance of each model and a way to rate it. The multi node model was run using from 6 to 96 spatial nodes; the plug flow model was run using 1 to 0.001 º C temperature bin sizes. Additionally, timesteps were varied from 4 to 236 s. The results quantify the tradeoff between accuracy and computational time, providing guidance for simulations to intelligently select the best model type and simulation parameters. This research can be used to validate the pre-existing models and possibly improve the modern water tank.

Bulnes, Fernando Karg↗
DOE OSTI2023

Foreign material exclusion plug for structural threads with breakaway tab

A foreign material exclusion plug is disclosed. The foreign material plug includes a cylindrical body, a slot, and a breakaway tab. The cylindrical body includes an externally-threaded cylindrical structure. The slot is formed in the cylindrical body extending into the cylindrical body from a top surface of the cylindrical body. The breakaway tab is connected to the cylindrical body and extends outwards from the top surface.

Cole, Robert N.↗
DOE OSTI2020

Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling

Plug-and-play priors (PnP) is an image reconstruction framework that utilizes an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the prior to be expressible in the form of a regularization function. This flexibility enables PnP algorithms to exploit the most effective image denoisers, leading to their state-of-the-art performance in various imaging tasks. However, many powerful denoisers, such as the ones based on convolutional neural networks (CNNs), do not have tunable parameters that would allow controlling their influence within PnP. To address this issue, in this paper, we introduce a scaling parameter that adjusts the magnitude of the denoiser input and output. We theoretical justify the denoiser scaling from the perspectives of proximal optimization, statistical estimation, and consensus equilibrium. Finally, we provide numerical experiments demonstrating the ability of denoiser scaling to systematically improve the performance of PnP for denoising CNN priors that do not have explicitly tunable parameters.

Boosting↗
DOE OSTI2020

Plug-In Electric Vehicle Showcases: Consumer Experience and Acceptance

In 2016 the U.S. Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy's Vehicle Technologies Office (VTO) announced three awardees to hold plug-in electric vehicle (PEV) showcases to demonstrate available technologies and provide a hands-on consumer experience at conveniently located, brand-neutral settings. The events varied in style from long term stationary storefront settings to weekend events at a variety of regional venues. Attendees could interact with the technology through ride-and-drives and longer-term test drives. The events began in the spring of 2017 and continued through 2019.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗