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DOE OSTI2020

Integration of amorphous ferromagnetic oxides with multiferroic materials for room temperature magnetoelectric spintronics

A room temperature amorphous ferromagnetic oxide semiconductor can substantially reduce the cost and complexity associated with utilizing crystalline materials for spintronic devices. We report a new material (Fe 0.66 Dy 0.24 Tb 0.1 ) 3 O 7-x (FDTO), which shows semiconducting behavior with reasonable electrical conductivity (~500 mOhm-cm), an optical band-gap (2.4 eV), and a large enough magnetic moment (~200 emu/cc), all of which can be tuned by varying the oxygen content during deposition. Magnetoelectric devices were made by integrating ultrathin FDTO with multiferroic BiFeO 3 . A strong enhancement in the magnetic coercive field of FDTO grown on BiFeO 3 validated a large exchange coupling between them. Additionally, FDTO served as an excellent top electrode for ferroelectric switching in BiFeO3 with no sign of degradation after ~10 10 switching cycles. RT magneto-electric coupling was demonstrated by modulating the resistance states of spin-valve structures using electric fields.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Computing x-ray absorption spectra from linear-response particles atop optimized holes

State specific orbital optimized density functional theory (OO-DFT) methods, such as restricted open-shell Kohn–Sham (ROKS), can attain semiquantitative accuracy for predicting x-ray absorption spectra of closed-shell molecules. OO-DFT methods, however, require that each state be individually optimized. In this Communication, we present an approach to generate an approximate core-excited state density for use with the ROKS energy ansatz, which is capable of giving reasonable accuracy without requiring state-specific optimization. Herein, this is achieved by fully optimizing the core-hole through the core-ionized state, followed by the use of electron-addition configuration interaction singles to obtain the particle level. This hybrid approach can be viewed as a DFT generalization of the static-exchange (STEX) method and can attain ~0.6 eV rms error for the K-edges of C–F through the use of local functionals, such as PBE and OLYP. This ROKS(STEX) approach can also be used to identify important transitions for full OO ROKS treatment and can thus help reduce the computational cost of obtaining OO-DFT quality spectra. ROKS(STEX), therefore, appears to be a useful technique for the efficient prediction of x-ray absorption spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Integrated Magnetics for Multiport Inductive and Conductive Power Transfer Architecture for Electric Vehicles

This paper proposes a shared magnetic arrangement for an electric-vehicle charger that supports both conductive and inductive charging while preserving independent power control at the two ports. In the proposed implementation, the high-frequency transformer used for the conductive path and the resonant inductor required by the inductive path are realized in an integrated form. By combining the magnetic structure and primary-side converter hardware, the charger can deliver improved hardware utilization and higher power density without a corresponding increase in cost or implementation complexity. The paper describes the magnetic realization and presents finite-element and circuit-level simulation results to confirm minimal interaction between the two charging paths. The study demonstrates that the proposed charger is a viable option for flexible, high-power EV charging systems.

Mukherjee, Subho [ORNL] (ORCID:0009000672297925)↗

DOE OSTI2022

Optimising the geospatial configuration of a future lithium ion battery recycling industry in the transition to electric vehicles and a circular economy

Rapid electrification of the transport system will generate substantial volumes of Lithium-ion-battery (LiB) waste as batteries reach their end-of-life. Much attention focuses on the recycling processes, neglecting a broader systemic view that considers the concentration of the costs and impacts associated with logistics and transportation. This paper provides an economic, environmental and geospatial analysis of a future LiB recycling industry in the UK. Hitherto, state-of-the-art assessment methods have evaluated life cycle impacts and costs but have not considered the geographical layer of the problem. This paper develops a GSC derived supply chain model for the UK electric vehicle and end-of-life vehicle battery industry. Considering both pyrometallurgical and hydrometallurgical recycling technologies, the optimisation process takes into account anticipated EV volumes, and, based on anticipated near-term technological evolution of LiBs, the evolution of the mix of battery cathodes in production, and presents a number of scenarios to show where LiB recycling facilities should ideally be geographically located. An economic and environmental assessment based on a customised EverBatt model is provided.

25 ENERGY STORAGE↗

DOE OSTI2026

Strategic Energy Plan: City of Key West, Florida

This Strategic Energy Plan for the city of Key West, Florida—developed through the U.S. Department of Energy’s Energy Technology Innovation Partnership Project (ETIPP)—outlines a comprehensive strategy to advance the city’s energy vision: to improve energy efficiency and independence using local energy resources to foster long-term resilience. To guide this effort, the plan is structured around four focus areas: • Energy efficiency: Reduce overall energy consumption and utility costs across municipal, residential, and commercial buildings. • Local energy generation: Increase the share of energy produced from local sources to enhance energy independence. • Resilience: Strengthen critical infrastructure and community preparedness for flooding, hurricanes, and other natural weather hazards. • Electric transportation: Support the addition of new electric vehicles (EVs) and develop reliable charging infrastructure to reduce reliance on imported fuels. ETIPP provides strategic energy planning, technical assistance, and direct funding to U.S. coastal, remote, and island communities to improve energy resilience. Key West was part of ETIPP’s fourth cohort. As a low-lying island community vulnerable to infrastructure damage due to natural weather hazards, Key West seeks to reduce its dependence on external energy and build long-term sustainability. The Strategic Energy Plan identifies specific challenges, sets clear goals, and proposes actionable opportunities with implementation timelines and key stakeholders. A baseline assessment reveals significant energy consumption in both city-owned and residential/commercial buildings, limited local energy generation, and an early-stage EV market with vulnerable charging infrastructure. The proposed solutions emphasize a multifaceted approach, leveraging both established and innovative technologies, while addressing financial, technical, and community engagement challenges. The baseline assessment provided a snapshot of current energy conditions in Key West, evidencing key challenges and opportunities across the city’s energy priorities. Building on the baseline assessment and extensive input from city staff, local stakeholders, and community partners, a set of targeted opportunities was identified to address Key West’s energy goals. Each was evaluated in terms of its potential benefits, key implementation steps, associated challenges and mitigation strategies, and the city departments and stakeholders best positioned to lead or support progress.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Cost Effective 6.5% Silicon Steel Laminate for Electric Machines

Iowa State University teamed with United Technologies Research Center and the University of Delaware and demonstrated a high switching frequency non-rare earth permanent magnet motor. A prototype motor with 400 Hz switching frequency, dummy MnBi permanent magnet with 8 MGOe energy density, and 6.5%Si steel as the laminate materials was designed, built, and tested. The performance of the prototype met the expectations showing a higher frequency motor enabled by 6.5%Si steel is a viable approach to meet DOE 2022 cost and performance targets for electric vehicle drive. Adoption of 6.5%Si steel will result in 3% improvement in energy conversion efficiency of the soft magnetic materials. In addition, the ability to use non-rare earth magnet for high power density application effectively mitigate the risk of EV traction motor from the rising rare earth price.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2015

Toward Head-Worn Displays for Equivalent Visual Operations

The Next Generation Air Transportation System represents an envisioned transformation to the U.S. air transportation system that includes an "equivalent visual operations" (EVO) concept, intended to achieve the safety and operational tempos of Visual Flight Rules (VFR) operations independent of visibility conditions. Today, Federal Aviation Administration regulations provide for the use of an Enhanced Flight Visual System (EFVS) as "operational credit" to conduct approach operations below traditional minima otherwise prohibited. An essential element of an EFVS is the Head-Up Display (HUD). NASA has conducted a substantial amount of research investigating the use of HUDs for operational landing "credit", and current efforts are underway to enable manually flown operations as low as 1000 feet Runway Visual Range (RVR). Title 14 CFR 91.175 describes the use of EFVS and the operational credit that may be obtained with airplane equipage of a HUD combined with Enhanced Vision (EV) while also offering the potential use of an “equivalent” display in lieu of the HUD. A Head-Worn Display (HWD) is postulated to provide the same, or better, safety and operational benefits as current HUD-equipped aircraft but for potentially more aircraft and for lower cost. A high-fidelity simulation was conducted that examined the efficacy of HWDs as "equivalent" displays. Twelve airline flight crews conducted 1000 feet RVR approach and 300 feet RVR departure operations using either a HUD or HWD, both with simulated Forward Looking Infra-Red cameras. The paper shall describe (a) quantitative and qualitative results, (b) a comparative evaluation of these findings with prior NASA HUD studies, and (c) describe current research efforts for EFVS to provide for a comprehensive EVO capability.

Prinzel, Lawrence (Lance) J., III↗

NASA NTRS2017

Transformational Solar Array Option I Final Report

This report summarizes the work performed under NASA contract NNC16CA19C from May 2, 2017 through April 2, 2018. This work is directed toward meeting the goals of the associated NASA NRA and, of course, the requirements of the contract. In brief, the goals are: (1) Over 47% beginning of life cell efficiency at 5 AU and -125 °C (2) Over 32% end of life efficiency at the blanket level at 50 W m-2, -125 °C and 4E15 1 MeV e cm-2 (3) Over 8 W kg-1 at EOL for the entire array including structure, deployment, and pointing mechanisms using beginning of life performance. (4) A stowed packaging density of greater than 66 kW m-3 (5) An ability to survive launch and numerous deploy retract cycles without degradation (6) An output higher than 300 V (7) An ability to operate in a plasma generated by xenon thrusters, typically 1E8 cm-3 ions with an average energy of 2 eV (8) A design compatible with electrostatic and magnetic cleanliness (9) Record breaking inverted metamorphic (IMM) 6 junction solar cells (10) IMM solar cells that have no anomalous flat spot behavior at low irradiance and low temperature (11) A mock-up production line for the low-cost manufacture of spacecraft blanket arrays. The Option I phase of the project continued efforts, started in the base-phase, to eliminate or reduce to very low levels the flat spots that reduce power to an unacceptable value in a significant percentage of cells and to reduce outgassing contamination of the concentrators to acceptable levels. Option I adds tasks to increase the efficiency of IMM cells from those produced in the Base Phase, to eliminate delamination of the coatings that were present in previous versions of the concentrator mirrors, to evaluate pressure sensitive adhesive as a method of fixing solar cell assemblies to blankets, to design a magnetically clean brake for ROSA, to test the robustness of a sample blanket in deploy and retract, to test for the adequate performance of a blanket in vibration and thermal environments, and to define the capital equipment needed to optimize production of the Transformational Array. 5 Work for this Final Report showed that the greatest likely improvement in the solar cells would be by emphasizing the effort for the IMM4 solar cells and stopping work on other IMM cells. For this phase, the solar cell work was primarily on the IMM4 cells with little work on IMM5 and none on IMM6 cells.

Gaddy, Edward↗

DOE OSTI2024

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗

NASA NTRS1985

Air fluorescence detection of large air showers below the horizon

In the interest of exploring the cosmic ray spectrum at energies greater than 10 to the 18th power eV, where flux rates at the Earth's surface drop below 100 yr(-1) km(-2) sr(-1), cosmic ray physicists have been forced to construct ever larger detectors in order to collect useful amounts of data in reasonable lengths of time. At present, the ultimate example of this trend is the Fly's Eye system in Utah, which uses the atmosphere around an array of skyward-looking photomultiplier tubes. The air acts as a scintillator to give detecting areas as large as 5000 square kilometers sr (for highest energy events). This experiment has revealed structure (and a possible cutoff) in the ultra-high energy region above 10 o the 19th power eV. The success of the Fly's Eye experiment provides impetus for continuing the development of larger detectors to make accessible even higher energies. However, due to the rapidly falling flux, a tenfold increase in observable energy would call for a hundredfold increase in the detecting area. But, the cost of expanding the Fly's Eye detecting area will approximately scale linearly with area. It is for these reasons that the authors have proposed a new approach to using the atmosphere as a scintillator; one which will require fewer photomultipliers, less hardware (thus being less extensive), yet will provide position and shower size information.

Halverson, P.↗

DOE OSTI2024

High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP): Design Guidelines and Specifications for DC Distribution-Based Charging Hub

Managed under U.S. Department of Energy (DOE)-funded EVs@Scale Consortium, High-Power Electric Vehicle Charging Hub Integration Platform (eCHIP) project aims to design and develop a high-power, interoperable charging experimental platform to research, develop, and demonstrate the integration and control approaches for a DC distribution-based high-power charging (HPC) system. The eCHIP project addresses the crucial need to design and validate efficient, low-cost, reliable, and interoperable solutions for DC-coupled charging hub ('DC hub' for short). This report explains the design, development, and implementation process of the experimental platform for the DC hub. The utilization of DC distribution holds significant potential for enhancing the operation of an HPC station architecture. However, there are challenges establishing the DC hub, including interoperability, commoditization, distributed energy resource integration, stability, DC protection, and lack of common system level controllers. To address these challenges, a testing setup is required that accommodates commercial off-the-shelf (COTS) products as well as novel, in-house designed solutions to evaluate different use cases at rated power and voltage levels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Battery Energy Storage Systems: A Review of Energy Management Systems and Health Metrics

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

Nazaralizadeh, Solmaz (ORCID:0009000737004039)↗

NASA NTRS1985

Evaluation of semiconductor devices for Electric and Hybrid Vehicle (EHV) ac-drive applications, volume 1

The results of evaluation of power semiconductor devices for electric hybrid vehicle ac drive applications are summarized. Three types of power devices are evaluated in the effort: high power bipolar or Darlington transistors, power MOSFETs, and asymmetric silicon control rectifiers (ASCR). The Bipolar transistors, including discrete device and Darlington devices, range from 100 A to 400 A and from 400 V to 900 V. These devices are currently used as key switching elements inverters for ac motor drive applications. Power MOSFETs, on the other hand, are much smaller in current rating. For the 400 V device, the current rating is limited to 25 A. For the main drive of an electric vehicle, device paralleling is normally needed to achieve practical power level. For other electric vehicle (EV) related applications such as battery charger circuit, however, MOSFET is advantageous to other devices because of drive circuit simplicity and high frequency capability. Asymmetrical SCR is basically a SCR device and needs commutation circuit for turn off. However, the device poses several advantages, i.e., low conduction drop and low cost.

Lee, F. C.↗

DOE OSTI2023

Bayesian Monte Carlo Evaluation of Imperfect (n, 233 U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations

This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.

Biswas, Shuchismita↗

NASA NTRS1995

Emergency vehicle alert system (EVAS)

The Emergency Vehicle Alert System (EVAS) program is sponsored by the NASA/MSFC Technology Utilization (TU) office. The program was conceived to support the needs of hearing impaired drivers. The objective of the program is to develop a low-cost, small device which can be located in a personal vehicle and warn the driver, via a visual means, of the approach of an emergency vehicle. Many different technologies might be developed for this purpose and each has its own advantages and drawbacks. The requirements for an acoustic detection system, appear to be pretty stringent and may not allow the development of a reliable, low-cost device in the near future. The problems include variations in the sirens between various types of emergency vehicles, distortions due to wind and surrounding objects, competing background noise, sophisticated signal processing requirements, and omni-directional coverage requirements. Another approach is to use a Radio Frequency (RF) signal between the Emergency Vehicle (EV) and the Personal Vehicle (PV). This approach requires a transmitter on each EV and a receiver in each PV, however it is virtually assured that a system can be developed which works. With this approach, the real technology issue is how to make a system work as inexpensively as possible. This report gives a brief summary of the EVAS program from its inception and concentrates on describing the activities that occurred during Phase 4. References 1-3 describe activities under Phases 1-3. In the fourth phase of the program, the major effort to be expended was in development of the microcontroller system for the PV, refinement of some system elements and packaging for demonstration purposes. An EVAS system was developed and demonstrated which used standard spread spectrum modems with minor modifications.

Reed, Bill↗

DOE OSTI2020

The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids: Preprint

The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. Different types of DERs such as Photovoltaics (PV), energy storage, electric vehicles (EVs), etc. have varying effects on the grid based on how and where they are deployed. Again, the DER deployment and its impact are network-dependent, while the traditional electric grids were not designed to host DERs. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper investigates the impacts of integrating a mix of DERs in the utility distribution network in Colorado, U.S. Firstly, BTM DERs at the residential scale including distributed PV; battery energy storage systems (BESS); heating, ventilation, and air-conditioning (HVAC) load; electric water heater (EWH) load; and EVs are modeled. The impacts of integrating these resources on the distribution network are evaluated by conducting time-series simulations for different scenarios considering different days to capture the worst-case conditions. Monte Carlo simulations are conducted to generate the realistic EV charging profile. The voltage issues, substation transformer loadings, and critical nodes in the network are identified with the incorporation of uncoordinated EV charging loads and other DER in the network. This analysis helps visualize the DER impact on the grid, identify the challenges, and provides an insight into the new distribution management and control needs to enable reliable and resilient distribution grid operations. Additionally, further analysis is performed to estimate the daily residential electricity cost with the inclusion of DERs under time-of-use tariffs. The result shows the daily residential electricity cost reduced by 20:7% on average with DERs compared to the case without DERs.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2021

Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy

A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet/vacuum ultraviolet emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, T e . This NN is shown to be particularly good at predicting T e at low temperatures (T e < 10 eV) where the NN demonstrated a mean average error of less than 1 eV. Trained to detect plasma detachment in the tokamak divertor, a NN classifier was able to correctly identify detached states (T e < 5 eV) with a 99% accuracy (an F 1 score of 0.96) at an acquisition rate 10× faster than the Thomson scattering measurement. The performance of the model is understood by examining a set of 4800 theoretical spectra generated using collisional radiative modeling that was also used to predict the performance of a low-cost spectrometer viewing nitrogen emission in the visible wavelengths. Furthermore, these results provide a proof-of-principle that low-cost spectrometers leveraged with machine learning can be used to boost the performance of more expensive diagnostics on fusion devices and be used independently as a fast and accurate T e measurement and detachment classifier.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗