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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Summary of Expansions and Updates in GREET® 2020

The GREET® (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model has been developed by Argonne National Laboratory with the support of the U.S. Department of Energy (DOE). GREET is a life-cycle analysis (LCA) tool, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail) and other end-use sectors, and energy systems. Argonne has expanded and updated the model in various sectors in GREET 2020, and this report provides a summary of the release.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Ten questions concerning energy flexibility in buildings

Demand side energy flexibility is increasingly being viewed as an essential enabler for the swift transition to a low-carbon energy system that displaces conventional fossil fuels with renewable energy sources while maintaining, if not improving, the operation of the energy system. Building energy flexibility may address several challenges facing energy systems and electricity consumers as society transitions to a low-carbon energy system characterized by distributed and intermittent energy resources. For example, by changing the timing and amount of building energy consumption through advanced building technologies, electricity demand and supply balance can be improved to enable greater integration of variable renewable energy. Although the benefits of utilizing energy flexibility from the built environment are generally recognized, solutions that reflect diversity in building stocks, customer behavior, and market rules and regulations need to be developed for successful implementation. In this paper, we pose and answer ten questions covering technological, social, commercial, and regulatory aspects to enable the utilization of energy flexibility of buildings in practice. In particular, we provide a critical overview of techniques and methods for quantifying and harnessing energy flexibility. We discuss the concepts of resilience and multi-carrier energy systems and their relation to energy flexibility. We argue the importance of balancing stakeholder engagement and technology deployment. Finally, we highlight the crucial roles of standardization, regulation, and policy in advancing the deployment of energy flexible buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cybersecurity for Fast Charging EV Infrastructure

The integration of electric vehicles (EVs) into electric grid operations can potentially leave the grid vulnerable to cyberattacks from both legacy and new equipment and protocols, including extreme fast-charging infrastructure. This paper introduces a co-simulation platform to perform cyber vulnerability analysis of EV charging infrastructure and its dependencies on communications and control systems. Grid impact scenarios through linkages to power system simulation tools such as OpenDSS and vehicle infrastructure-specific attack paths are discussed. An adaptive platform that assists with predicting and solving evolving cybersecurity challenges is demonstrated with a cyber-energy emulation that accelerates the analysis of cyberattacks and system behavior.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2021

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale distribution system of 11,000 nodes, the DERMS software controller, and 90 power hardware photovoltaics (PVs) and battery inverters. The applied DERMS algorithm is designed based on a realtime optimal power flow algorithm and implemented with acceleration design that performs fast dispatch of simulated PVs and real physical hardware DER devices every 4 seconds.

DERMS↗
DOE OSTI2024

Next-Generation Desiccant-Based Gas Clothes Dryer Systems

The goal of the project is to develop an advanced gas clothes dryer system demonstrating significant improvements in fuel efficiency and drying performance compared to current gas clothes dryer technologies. The new system decouples latent and sensible loads to effectively utilize the latent heat associated with laundry moisture as an advantageous energy source. In other words, the system captures the waste latent heat from moisture produced during the fabric-drying process and reuses it to improve drying efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Case Study: Applying the INL Resilience Framework to Iowa Lakes Electric Cooperative Distributed Wind Systems

Traditional metrics and evaluation methods for resiliency are not sufficient to evaluate the effect that distributed wind systems will have, particularly in light of the challenges described above. While the concept of resiliency is not new, its application to the electric grid is neither standardized nor well-defined, and there is little to no guidance on how to evaluate resilience specifically for distributed wind systems. To fill this gap, the Idaho National Laboratory (INL), as part of the multi-laboratory Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project, has developed a resilience framework for electric energy delivery systems (EEDS). The framework provides detailed steps for evaluating resiliency in the planning, operational, and future stages, and encompasses five core functions of resilience. It allows users to evaluate the resilience of distributed wind, taking into consideration the resilience of the wind systems themselves, as well as the effect they have on the resiliency of any systems they are connected to. In this study, we evaluate the resilience of the distributed wind systems at Iowa Lakes Electric Cooperative to cybersecurity hazards. We show that the wind resource can benefit the overall system resilience during some hazards. We show that the practices in place make the wind subsystems resilient against some cybersecurity hazards but that there are still significant risks associated with other cybersecurity hazards

17 WIND ENERGY↗
DOE OSTI2021

A KBase Case Study on Genome-wide Transcriptomics and Plant Primary Metabolism in Sorghum

A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by allowing them to upload private or public data into the KBase Narrative Interface, empowering them to analyze this data using a rich, extensible array of computational and data-analytics tools, and allows them to securely share scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes that are differentially expressed during drought-stress in Sorghum bicolor, which is an important sustainable production crop plant. We then show how to use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2025

Designing Future Energy Systems with Generative AI

Energy systems are experiencing various changes that impact the distribution, use, and reliability of energy. Local utilities and municipalities must respond and adapt to these changes, moving towards a future energy system with modernized infrastructure and other targeted investments and policy decisions. However, planning for and enacting these advancements requires significant effort from experts and engineers to develop strategies that ensure a reliable and secure energy future. This includes characterizing the current energy infrastructure, identifying areas for development, and engaging with local community members. Emerging generative artificial intelligence techniques can alleviate pain points and help support the development of the next generation of energy systems. Here, in this article, we highlight on-going generative AI work in the areas of atmospheric modeling, building energy management, and distribution network design, and we propose a vision for the role of generative AI that considers opportunities and identifies challenges inherent to this technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Advancement and Application

This report documents activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2024 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, Digital Instrumentation and Control (DI&C) Risk Assessment project. The goal of the RISA Pathway is to optimize safety margins and minimize uncertainties to achieve economic efficiencies while maintaining high levels of safety. This is accomplished by providing scientific basis to better represent safety margins and factors that contribute to cost and safety, and by developing new technologies that reduce operating costs. The research efforts for FY 2024 encompass methodology refinement and exploration. The efforts include: (1) The implementation of a natural language processing tool to expedite key aspects of the reliability analysis methods developed by INL; (2) advances to support intersystem CCF analysis by providing guidance for and identification of coupling mechanisms that may contribute to CCF; (3) the investigation of how generative artificial intelligence tools can aid in hazard analysis and diversity and defense in depth (i.e., D3) assessments; (4) Industry collaboration, allowing the demonstration of and INL's risk assessment tools to support risk assessment of DI&C systems at early and late stages of development; (4) a roadmap for the development of a software for each of INL's risk assessment tools; (5) The development of a theory and methodology manual for a risk quantification methodology; (6) the development of a reliability analysis for machine learning (ML)-integrated control systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory envi-ronment, including the accurate modeling of a full-scale dis-tribution system of 11,000 nodes, the DERMS software con-troller, and 90 power hardware photovoltaics (PVs) and bat-tery inverters. The applied DERMS algorithm is designed based on a real-time optimal power flow algorithm and im-plemented with acceleration design that performs fast dis-patch of simulated PVs and real physical hardware DER devices every 4 seconds.

distributed energy management system (DERMS)↗
DOE OSTI2024

Evaluation of Mechanical and Thermomechanical Water Vapor Compression Techniques for Enabling High Temperature Lift Hydration-Based Chemical Heat Pumps

Achieving high temperature lifts (>200 K) via a chemical heat pump based on salt hydration/dehydration reactions requires the transport of water vapor from low to high pressure. Alternative compression approaches require condensing of low-pressure water vapor, pumping of liquid water, and subsequent evaporation when the low-side pressure corresponds to sub-ambient water saturation temperatures. Thus, this study compares four steam compression methods for use within a chemical heat pump system based on a reversible calcium oxide hydration/dehydration reaction with a temperature lift from 350 °C heat to >600 °C. Purely mechanical and thermochemical/mechanical compression technologies are considered. A parametric study of maximum allowable temperature, the isentropic efficiency of mechanical compressors, the effectiveness of heat exchangers, and the assumed allowable heat exchanger pressure drop is conducted to determine the mechanical and thermal energy consumed per kilogram of compressed steam. The system complexity in terms of the number of main system components, maximum pressure ratio, and maximum allowable temperature is estimated. Model results show an absorption-based steam compressor has the highest exergetic efficiency for the required chemical heat pump required conditions. As a result, this system configuration was then experimentally demonstrated to illustrate the impact of system performance on component effectiveness.

Advanced reactors↗
DOE OSTI2025

Theoretical foundations of waste factor and waste figure with applications to fixed wireless access and relay systems

The growing energy demands of next-generation wireless systems call for unified, system-level metrics to evaluate and optimize energy efficiency. This paper advances the concept of the Waste Factor (W), or Waste Figure (WF) in decibel scale, as a general framework for modeling power loss across cascaded communication components. By integrating W into the Consumption Factor (CF)–the ratio of data rate to consumed power–we reveal how component inefficiencies influence the minimum achievable energy per bit. Closed-form expressions are derived for energy-per-bit consumption in both direct and relay-assisted links, along with a decision rule for selecting the more energy-efficient path. While not explicitly modeled, Reflective Intelligent Surfaces (RIS) are shown to fit naturally within this framework. The analysis is further applied to a Fixed Wireless Access (FWA) scenario, where asymmetries in traffic direction and hardware inefficiencies are jointly considered, demonstrating the utility of the Waste Factor in guiding energy-aware system design.

Sevim, Nurullah [Texas A&M University, College Sta↗
DOE OSTI2024

Securing Critical Materials for the U.S. Electric Vehicle Industry: A Landscape Assessment of Domestic and International Supply Chains for Five Key Battery Materials

This study explores the prospective supply of upstream critical materials, providing insights into the U.S.'s capacity to meet its Electric Vehicle (EV) and Energy Storage System (ESS) deployment targets for 2035. It evaluates the proportion of critical materials demand that can be met by domestic upstream sources and the amount that will require non-U.S. sources. The analysis considers geological resources and current international development activities, contributing to the understanding of mineral supply security as the global community strives for net-zero emissions by 2050. The study focuses on five materials assessed in the 2023 DOE Critical Materials Assessment – Lithium, Nickel, Cobalt, Graphite, and Manganese.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Unified architecture for data-driven metadata tagging of building automation systems

This article presents a Unified Architecture (UA) for automated point tagging of Building Automation System (BAS) data, based on a combination of data-driven approaches. Advanced energy analytics applications—including fault detection and diagnostics and supervisory control—have emerged as a significant opportunity for improving the performance of our built environment. Effective application of these analytics depends on harnessing structured data from the various building control and monitoring systems, but typical BAS implementations do not employ any standardized metadata schema. While standards such as Project Haystack and Brick Schema have been developed to address this issue, the process of structuring the data, i.e., tagging the points to apply a standard metadata schema, has, to date, been a manual process. This process is typically costly, labor-intensive, and error-prone. In this work we address this gap by proposing a UA that automates the process of point tagging by leveraging the data accessible through connection to the BAS, including time-series data and the raw point names. The UA intertwines supervised classification and unsupervised clustering techniques from machine learning and leverages both their deterministic and probabilistic outputs to inform the point tagging process. Furthermore, we extend the UA to embed additional input and output data-processing modules that are designed to address the challenges associated with the real-time deployment of this automation solution. We test the UA on two datasets for real-life buildings: (i) commercial retail buildings and (ii) office buildings from the National Renewable Energy Laboratory (NREL) campus. We report the proposed methodology correctly applied 85–90% and 70–75% of the tags in each of these test scenarios, respectively for two significantly different building types used for testing UA's fully-functional prototype. The proposed UA, therefore, offers promising approach for automatically tagging BAS data as it reaches close to 90% accuracy. Further building upon this framework to algorithmically identify the equipment type and their relationships is an apt future research direction to pursue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A Data-Driven Voltage Control Strategy for Distribution Grids With Distributed Energy Resources

Traditionally, distribution system control approaches have been model-based. The deployment of advanced metering infrastructure has provided electric utilities with the capability of data-driven control with real-time measurements. The shift from model-based to data-driven control represents a significant advancement in the management of distribution systems, offering a more adaptive approach to system control because of the ability to dynamically adapt to changing conditions without the need for system modeling. Here, in this paper, a behavioral data-driven control method is developed to provide voltage regulation to an actual distribution system by controlling the legacy devices and distributed energy resource (DER) assets. The studied distribution system has a load tap changer and three capacitor banks as the legacy devices and photovoltaic systems as the DERs. The performance of the proposed control algorithm is validated using a laboratory test bed setup considering multiple scenarios. The results show that the proposed control achieved 99% voltage regulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Summary of Expansions and Updates in R&D GREET ® 2023

The Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET®) model was developed by Argonne National Laboratory (Argonne) with the support of the U.S. Department of Energy (DOE) and other federal agencies. R&D GREET is a life cycle analysis (LCA) model, structured to systematically examine the energy and environmental effects of a wide variety of transportation fuels and vehicle technologies in major transportation sectors (i.e., road, air, marine, and rail), other end-use sectors, and energy systems. Argonne has expanded and updated the model in various areas in R&D GREET 2023. This report provides a summary of the expansions and updates.

33 ADVANCED PROPULSION SYSTEMS↗