Results for “Plugs” — Page 5

DOE OSTI2022

Development of a plug-and-play anti-noise module for fault diagnosis of rotating machines in nuclear power plants

The health of rotating machines is crucial to the stable operation and safety of nuclear power plants. However, research on machine learning-based fault diagnosis of rotating machines in the nuclear industry is still in its infancy. The signal noise generated in the plant may negatively affect the effectiveness of the analysis. In this paper, a plug-and-play anti-noise machine learning module is proposed to fill the knowledge and capability gap. The modules are loaded into a convolutional neural network called deep residual network (ResNet) to obtain a new model with noise reduction capability. The basic idea is that the module is able to identify noise features and include them in the subsequent analysis, effectively filtering noise at the feature level of the network. Nine variants of the new model are compared with the original ResNet as well as four classical machine learning models to test the effectiveness of the module and to examine the impact of the module's loading modes on the performance of the new model. Finally, this research helps facilitate the application of machine learning in the plant noise environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Well-to-Wheels Analysis of Zero-Emission Plug-In Battery Electric Vehicle Technology for Medium- and Heavy-Duty Trucks

Conventional diesel medium- and heavy-duty vehicles (MHDVs) create large amount of air emissions. With the advancement in technology and reduction in the cost of batteries, plug-in battery electric vehicles (BEVs) are increasingly attractive options for improving energy efficiency and reducing air emissions of MHDVs. In this paper, we compared the well-to-wheels (WTW) greenhouse gases (GHGs) and criteria air pollutant emissions of MHD BEVs with their conventional diesel counterparts across weight classes and vocations. We expanded the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model to conduct the WTW analysis of MHDVs. The fuel economy for a wide range of MHDV weight classes and vocations, over various driving cycles, was evaluated using a high-fidelity vehicle dynamic simulation software (Autonomie). The environmental impacts of MHD BEVs are sensitive to the source of electricity used to recharge their batteries. The WTW results show that MHD BEVs significantly improve environmental sustainability of MHDVs by providing deep reductions in WTW GHGs, nitrogen oxides, volatile organic compounds, and carbon monoxide emissions, compared to conventional diesel counterparts. Increasing shares of renewable and natural gas technologies in future national and regional electricity generation are expected to reduce WTW particulate matters and sulfur oxide emissions for further improvement of the environmental performance of MHD BEVs.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Improved wall-plug efficiency of III-nitride tunnel junction micro-light-emitting diodes with AlGaN/GaN polarization charges

The electrical performances of III-nitride blue micro-light-emitting diodes (µLEDs) with different tunnel junction (TJ) epitaxial architectures grown by metalorganic chemical vapor deposition are investigated. A new TJ structure that employs AlGaN is introduced. The current density–voltage characteristic is improved by incorporating AlGaN layer above the n-side of the TJ layer, and the effects of the AlGaN/GaN superlattices is examined. Based upon the data from band diagram simulation, net positive polarization charge is formed at the AlGaN/GaN interface, which results in a reduction in tunneling distance and increase in tunneling probability. Moreover, similar electrical improvement is observed in various device dimensions and is independent of operating current density, suggesting that AlGaN/GaN biaxial tensile strain or current spreading is not the main contribution for the improvement. Finally, the effects on the efficiency performances are determined. While the maximum external quantum efficiency of the TJ devices remains identical, the wall-plug efficiency of µLEDs is enhanced significantly by the proposed AlGaN-enhanced TJ design. This work reveals the possibility of energy efficient TJ contact with high transparency in the visible wavelength range.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Secondary Use-Plug-and-Play Energy Storage System Composed of Multiple Energy Storage Technologies

Low-cost, grid-connectable energy storage technologies represent a significant challenge for the electric grid of the future. Energy storage technologies are in rapid development with targets to reduce the storage medium cost. However, a significant cost to deployment also comes in the integration. This paper presents the development of a plug-and-play system for supporting secondary use multiple battery systems into a single grid connectable unit. Results of the system design are demonstrated in a controller hardware in the loop (CHIL) platform. Simulations of two energy storage systems operating in parallel and dispatched optimally are presented.

Starke, Michael↗

DOE OSTI2023

Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. Additionally, we also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Ecological Adaptive Cruise Control of Plug-In Hybrid Electric Vehicle With Connected Infrastructure and On-Road Experiments

Abstract This paper examines both mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. Human errors are typical sources of accidents in urban driving, which can be remedied by rigorous control theories. Designing an ECO-ACC is, therefore, a classical research problem to improve safety and energy efficiency. We add two main contributions to the literature. First, we propose a mathematical framework of an online ECO-ACC for plug-in hybrid electric vehicle (PHEV). Second, we demonstrate ECO-ACC in a real world, which includes other human drivers and uncertain traffic signals on a 2.6 (km) length of the corridor with eight signalized intersections in Southern California. The demonstration results show, on average, 30.98% of energy efficiency improvement and 8.51% additional travel time.

Automation & Control Systems↗

DOE OSTI2020

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2019

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2019, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.4 million PEVs have been sold, driving over 37 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.34% in 2019 and 1.4 billion gallons cumulatively through 2019. In 2019, PEVs used 4.1 terawatt-hours of electricity to drive 12.7 billion miles, offsetting 470 million gallons of gasoline. Since 2010, 69% all PEVs have been assembled in the United States, and over 60 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Assessment of Light-Duty Plug-in Electric Vehicles in the United States, 2010 – 2019

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2019, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.4 million PEVs have been sold, driving over 37 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.34% in 2019 and 1.4 billion gallons cumulatively through 2019. In 2019, PEVs used 4.1 terawatt-hours of electricity to drive 12.7 billion miles, offsetting 470 million gallons of gasoline. Since 2010, 69% all PEVs have been assembled in the United States, and over 60 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Used Plug-in Electric Vehicles as a Means of Transportation Equity in Low-Income Households

This report examines improving the equity of low-income households through access to reliable means of transportation. Used plug-in electric vehicles (PEVs) can serve as a low-cost and low-maintenance means of transport for low-income households. Zero tail-pipe emissions from PEVs is also a benefit of these drivetrain compared to internal combustion engine vehicles (ICEVs). Barriers to the adoption of the used PEVs, and incentives that may address these barriers were reviewed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Status Report on IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENviroment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report provides discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity is maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In addition to the investigative work being conducted on FMUs and FMIs, a series of updates to the hybrid repository has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and opensourcing the HYBRID repository with an integrated regression system.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Final Technical Report-WestSmart EV: Western Smart Plug-in Electric Vehicle Community Partnership

The WestSmartEV (WSEV) project has accelerated adoption of plug-in electric vehicles (PEV) throughout the PacifiCorp/Rocky Mountain Power’s (RMP) service territory in the intermountain west by developing a large-scale, sustainable PEV charging infrastructure network with coordinated PEV adoption programs. The project objectives have strategically deployed 79 DC fast charging to create two primary electric interstate highway corridors along I-15 and I-80; incentivized installation of Level 2 AC chargers at workplace locations; incentivized the purchase of PEVs; provided all electric solutions for first-mile and last-mile trips, including electrified mobility service; provided centralized data collection, analysis, modeling, and tool development to inform investment and policy decisions; and developed education outreach materials and conducted workshops across the WSEV region.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Assessment of Light-Duty Plug-in Electric Vehicles in the United States (2010 - 2020)

This report examines properties of plug-in electric vehicles (PEVs) sold in the United States from 2010 to 2020, exploring vehicle sales, miles driven, electricity consumption, petroleum reduction, vehicle manufacturing, and battery production, among other factors. Over 1.7 million PEVs have been sold, driving 52 billion miles on electricity since 2010, thereby reducing national gasoline consumption by 0.42% in 2020 and 1.9 billion gallons cumulatively through 2020. In 2020, PEVs used 4.4 terawatt-hours of electricity to drive 13.7 billion miles, offsetting 500 million gallons of gasoline. Since 2010, 68% of PEVs sold in the United States have been assembled domestically, and 77 gigawatt-hours of lithium-ion batteries have been installed in vehicles to date.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Development of Electro-chemical Battery Model for Plug-and-Play Eco-system Library

Energy storage components are fundamental to the concept of an Integrated Energy System (IES). They serve to store surplus energy during low-demand periods for later release when other IES components (i.e., Secondary Energy Source, Balance of Plant, etc.) would otherwise have to operate flexibly. This provision for storage avoids high-amplitude power ramps in these components thereby limiting thermal and mechanical stresses to their internals and providing for extended service life. This report describes a dynamic model that has been developed for an electrochemical battery. The lithium-ion (Li-ion) cell was selected as representative technology. The battery model was developed in the Dymola simulation environment and meets the requirements of the ecosystem plug-and-play library. The model accurately describes the electric dynamic response of a Li-ion battery for an imposed charging/discharging power profile. The corresponding physical limitations related to over-power scenarios, and the impact of the residual state of charge are accounted for in the model. A literature review of the major degradation processes affecting Li-ion batteries was performed. Given the purposes of the CTD-IES project, the progressive fade of the installed capacity, the reduction of the round-trip efficiency, and the limits on the number of charging/discharging cycles are aspects that need to be taken into account in techno-economic analyses. The modeling of these degradation phenomena becomes crucial when predictions over long time horizons (capacity expansion) are made. For each one of these phenomena, a brief description is given, and some figures to be implemented in the HERON optimization algorithm are presented.

25 ENERGY STORAGE↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Domestic Sales Mix of Plug-In Electric Vehicles by Trim Variant and Vehicle Characteristics

As plug-in electric vehicles (PEVs) take up a larger share of the United States vehicle market, their variety increases as well. Since many electric vehicle manufacturers offer multiple PEV trim variants, the distribution of vehicle characteristics such as battery capacity, all-electric range, electricity consumption, and curb weight cannot be determined by sales data alone. Sales data and vehicle registration data were analyzed to quantify and map the trim variant distribution of PEV models from nine automakers. With this information, we quantify national sales-weighted characteristics of fourteen different models over ten years, including battery capacity, fuel economy, and vehicle weight. We find a positive correlation between share of all-wheel drive variants for Tesla vehicles and average annual snowfall, and general uniformity nationwide in other vehicle characteristics. The estimated trim variant distributions will be useful in informing decisions regarding materials recycling and EV impact on the energy grid, and this study found that the total installed battery capacity in PEV in the U.S. was 76 GWh through 2020.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

14 SOLAR ENERGY↗

DOE OSTI2021

Status Report on the INL IES Plug-and-Play Framework

This report discusses the advancements and status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and usage, which aims to ease the sharing and simulation of complex dynamic models. This report discusses the FMI/FMU development advancements, overall focusing on the deployment of methodologies for RAVEN to export and use FMI/FMU of both Python-based models and advanced AI-constructed algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Regional Variation in Light-Duty Plug-in Electric Vehicle Emissions

Automobile electrification is viewed as one of the key requirements for deep decarbonization of transportation. However, the exact magnitude of decarbonization from switching from a combustion of petroleum-based fuels to grid-derived electricity is a complex issue. Variations in local grid mixes lead to differing carbon intensities of electricity in different regions. This complexity is compounded by geographical differences in vehicle characteristics. This analysis considers these geographic differences to produce a historical assessment of the fuel-cycle carbon emissions of plug-in electric vehicles (PEVs) in the United States from 2011 to 2021. We find that PEVs in the United States decreased in electricity-derived carbon intensity, from 187 grams per mile to 110 grams per mile from 2011 to 2021 due to improvements in the electric grid and vehicle efficiency. Through 2021, PEVs emitted a total of 13.6 million metric tons of greenhouse gas (GHG) emissions in the United States, including both emissions from electricity and gasoline, while driving a total of 100 billion miles. Of this total, 8.39 million metric tons was due to electricity consumption. Over the same distance, comparable gasoline vehicles would have emitted between 29 and 41 million metric tons of GHG, leading to a reduction of 15 to 27 million metric tons. Relative to national-level assessments that do not factor in this regional difference, these electric vehicles resulted in 13% greater reductions in GHG emissions than previously calculated. In the future, announced goals to further decarbonize the electricity sector will continue to reduce the emissions of PEVs, including reducing emissions rates for vehicles already on the road, further improving the benefits relative to the unchanging emissions of contemporaneous gasoline vehicles.

02 PETROLEUM↗