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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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This handbook answers basic questions about plug-in electric vehicles, charging stations, charging equipment, charging equipment installation, and training for electrical contractors.
Increased charging needs from widespread adoption of battery electric vehicles (EVs) will impact electricity demand. This will likely require a combination of potentially costly distribution infrastructure upgrades and synergistic grid-transportation solutions such as managed charging and strategic charger placement. Fully implementing such strategic planning and control methods - including business models and mechanisms to engage and compensate consumers - can minimize or even eliminate required grid upgrades. Moreover, there are also opportunities for EV charging to support the grid by helping solve existing and emerging distribution system challenges associated with increasing distributed energy resources (DERs) such as solar generation and battery energy storage. This paper reviews the potential impacts of EV charging on electricity distribution systems and describes methods from the literature to efficiently integrate EVs into distribution systems.
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We present a simple method to estimate the maximum possible electrical energy saving that might be achieved by increasing the albedo of surfaces in a large city. We restrict this to the “indirect effect”, the cooling of outside air that lessens the demand for air conditioning (AC). Given the power demand of the electric utilities and data about the city, we can use a single linear equation to estimate the maximum savings. For example, the result for an albedo change of 0.2 of pavements in a typical warm city in California, such as Sacramento, is that the saving is less than about 2 kWh per m2 per year. This may help decision makers choose which heat island mitigation techniques are economical from an energy-saving perspective.
Quantum sensing affords the possibility of using quantum entanglement to probe electromagnetic fields with exquisite sensitivity. In this work, we show that a photogenerated spin-correlated radical ion pair (SCRP) can be used to sense an electric field change created at one radical ion of the pair using molecular recognition. The SCRP is generated within a covalent donor–chromophore–acceptor system PXX–PMI–NDI, 1, where PXX = peri-xanthenoxanthene, PMI = 1,6-bis(p-t-butylphenoxy)perylene-3,4-dicarboximide, and NDI = naphthalene-1,8:4,5-bis(dicarboximide). The electron-rich PXX donor in 1 acts as a guest molecule that can be encapsulated selectively by a tetracationic cyclophane ExBox4+ host to give a supramolecular complex 1 ⊂ ExBox 4+ . Selective photoexcitation of the PMI chromophore results in ultrafast generation of the PXX •+ –PMI–NDI •– SCRP. When PXX is encapsulated by ExBox 4+ , the cyclophane generates an electric field that repels the positive charge on PXX •+ within PXX •+ –PMI–NDI •– , reducing the SCRP distance, i.e., the distance between the centers-of-charge on the donor and acceptor. Pulse-EPR measurements are used to measure the coherent oscillations created primarily by the electron–electron dipolar coupling in the SCRP, which yields the distance between the two charges (spins) of PXX •+ –PMI–NDI •– . Here, the experimental results show that the distance between PXX •+ and NDI •– decreases when ExBox 4+ encapsulates PXX •+ , which demonstrates that the SCRP can function as a quantum sensor to detect electric field changes in the vicinity of the radical ions.
Anisotropy of biological tissues is a low-frequency phenomenon that is associated with the function and structure of cell membranes. Imaging of anisotropic conductivity has potential for the analysis of interactions between electromagnetic fields and biological systems, such as the prediction of current pathways in electrical stimulation therapy. To improve application to the clinical environment, precise approaches are required to understand the exact responses inside the human body subjected to the stimulated currents. In this study, we experimentally evaluate the anisotropic conductivity tensor distribution of canine brain tissues, using a recently developed diffusion tensor-magnetic resonance electrical impedance tomography method. At low frequency, electrical conductivity of the biological tissues can be expressed as a product of the mobility and concentration of ions in the extracellular space. From diffusion tensor images of the brain, we can obtain directional information on diffusive movements of water molecules, which correspond to the mobility of ions. The position dependent scale factor, which provides information on ion concentration, was successfully calculated from the magnetic flux density, to obtain the equivalent conductivity tensor. By combining the information from both techniques, we can finally reconstruct the anisotropic conductivity tensor images of brain tissues. The reconstructed conductivity images better demonstrate the enhanced signal intensity in strongly anisotropic brain regions, compared with those resulting from previous methods using a global scale factor.
Ge-Sb-Te-based phase-change memory is one of the most promising candidates to succeed the current flash memories. The application of phase-change materials for data storage and memory devices takes advantage of the fast phase transition (on the order of nanoseconds) and the large property contrasts (e.g., several orders of magnitude difference in electrical resistivity) between the amorphous and the crystalline states. Despite the importance of Ge-Sb-Te alloys and the intense research they have received, the possible phases in the temperature-pressure diagram, as well as the corresponding structure-property correlations, remain to be systematically explored. In this study, by subjecting the amorphous Ge{sub 2}Sb{sub 2}Te{sub 5} (a-GST) to hydrostatic-like pressure (P), the thermodynamic variable alternative to temperature, we are able to tune its electrical resistivity by several orders of magnitude, similar to the resistivity contrast corresponding to the usually investigated amorphous-to-crystalline (a-GST to rock-salt GST) transition used in current phase-change memories. In particular, the electrical resistivity drops precipitously in the P = 0 to 8 GPa regime. A prominent structural signature representing the underlying evolution in atomic arrangements and bonding in this pressure regime, as revealed by the ab initio molecular dynamics simulations, is the reduction of low-electron-density regions, which contributes to the narrowing of band gap and delocalization of trapped electrons. At P > 8 GPa, we have observed major changes of the average local structures (bond angle and coordination numbers), gradually transforming the a-GST into a high-density, metallic-like state. This high-pressure glass is characterized by local motifs that bear similarities to the body-centered-cubic GST (bcc-GST) it eventually crystallizes into at 28 GPa, and hence represents a bcc-type polyamorph of a-GST.
This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.
Hourly one-week electricity demand profiles for electric transit bus depots in the United States, as described in Liu et al. (2025). Please cite as: Liu, Bo, Tim Jonas, Kara Podkaminer, and Brennan Borlaug. 2025. Hourly Load Profile Dataset for Electric Transit Bus Depots in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-92140. https://www.nlr.gov/docs/fy25osti/92140.pdf
Advances in electric machine efficiency and energy storage capability are enabling a new alternative to traditional propulsion systems for aircraft. This has already begun with several small concept and demonstration vehicles, and NASA projects this technology will be essential to meet energy and emissions goals for commercial aviation in the next 30 years. In order to raise the Technology Readiness Level of electric propulsion systems, practical integration and performance challenges will need to be identified and studied in the near-term so that larger, more advanced electric propulsion system testbeds can be designed and built. Researchers at NASA Armstrong Flight Research Center are building up a suite of test articles for the development, integration, and validation of these systems in a real world environment.
Presentation to Aerospace Electrical Systems Expo, providing an overview of NASA's work on hybrid electric and all electric propulsion and projecting technology needs.
Electrifying Class-8 heavy-duty trucks presents a promising opportunity to enhance energy efficiency and reduce freight transport costs. Battery electric trucks (BETs), once considered niche, are gaining traction due to advancements in battery technology and cost reductions. However, accurately predicting battery lifespan under realistic usage conditions remains a key challenge. Understanding battery failure mechanisms and their links to design, operation, and management is essential for developers and fleet operators. This study introduces a method to develop simplified, lab-testable dynamic stress test (DST) cycles for regional and long-haul Class-8 BETs, derived from real-world diesel truck usage. These DSTs enable benchmarking of battery technologies, identification of aging stressors, and optimization of battery design, life, and cost. The approach supports evaluation of key metrics such as levelized cost of driving and total cost of ownership, aiding fair comparisons and adoption decisions. We also propose feasible battery pack sizes that meet current driving demands with strategic charging, and a method to scale pack-level DSTs to cell-level cycles for lab-based testing. These tools facilitate tradeoff analysis across battery chemistries, pack sizing, and charging strategies, while offering means to get insights into battery aging under realistic conditions-ultimately supporting informed BET deployment decisions.
In this study, the results of NREL's continued work on experimental characterization of the thermal performance of free-surface jets of automatic transmission fluid impinged on a heated target surface are presented. The measured heat transfer coefficients are useful for understanding factors influencing performance of driveline fluid-based cooling systems for electric machines and help designers in developing high-performance, power-dense and reliable machines. Experiments were carried out for different fluid and target surface temperatures (50 degrees C, 70 degrees C, and 90 degrees C for the fluid and 90 degrees C, 100 degrees C, 110 degrees C, and 120 degrees C for the target surface). Impinging jet velocities (0.5 m/s to 7.5 m/s) and the jet position on the target surface (center versus edge) were also varied. The impinging angle was kept at 90 degrees relative to the target surface. It was found that higher target surface temperature increased heat transfer coefficients, namely, increasing surface temperature from 90 degrees C to 120 degrees C enhanced heat transfer coefficient values at higher impinged jet velocities (7.5 m/s) by up to 15%.
This handbook answers basic questions about plug-in electric vehicles, charging stations, charging equipment, charging equipment installation, and training for electrical contractors.
Responses of the AC and DC global electric circuits (GECs) to the large eruption of the Hunga Tonga-Hunga Ha'apai (HT-HH) volcano on 15 January 2022 are discussed. The AC-related investigation is based on Schumann resonance (SR) measurements from six stations on four continents. The DC-related investigation utilizes atmospheric electric field (potential gradient, PG) measurements from six recording stations in Europe and the USA. According to data from the GLD360 and WWLLN lightning detection networks, the peak lightning stroke rate, 83/s, was dominated by negative polarity lightning, but the distributions of positive and negative lightning discharges in latitude and longitude around the volcano differed. A global intensification of SR is apparent in connection with the enhanced lightning activity caused by the eruption. SR data-based results confirm that the lightning activity in the eruption dominated the naturally occurring global activity for a period of about 1 hr. The highly localized increase in lightning activity over HT-HH was a unique point source of SR excitation. PG measurements suggest that impulse-like charging of the DC GEC, by ~15%, via negative cloud-to-ground lightning strokes took place twice during the eruption. A time constant of 7 or 8 min has been inferred for near-surface PG changes due to these enhancements. This could be the first direct measurement of the time constant of the GEC near the Earth's surface, as well as the first observation of the direct charging of the DC GEC by a unique atmospheric electrified source.
The degradation of the characteristics of silicon metal-oxide-semiconductor (MOS) structures with oxides of rare-earth elements under the effect of electric fields with intensities of 0.1–4 MV/cm during the course of electroforming is studied. A specific feature of electroforming consists in the possibility of multiple switching of the structures from the insulating state to the low-resistivity one and back. The temporal characteristics of the degradation of MOS structures during the course of electroforming are exponential. The current-voltage characteristics follow the power law in the range of 0.2–3 V; the effect of an electric field brings about a variation in the distribution of the energy density of traps responsible for currents limited by space charge. It is established that multiple cycles of electroforming lead to an increase in the density of surface states at the Si-oxide interface and to a variation in the energy position of the trap levels, which affects the charge state of the traps.
Thermal constraints place significant limitations on how electric motors ultimately perform. Finite element analysis and computational fluid dynamics modeling approaches are being increasingly utilized in the design and analysis of electric motors. As the models become more sophisticated, it is important to have detailed and accurate knowledge of material thermal properties and convective heat transfer coefficients. In this work, the thermal properties and inter-lamination thermal contact resistances were measured for different stator lamination materials. Also, convective heat transfer coefficients of automatic transmission fluid (ATF) jets were measured to better understand the heat transfer of ATF impinging on motor copper windings. Experiments were carried out at various ATF temperatures and jet velocities to quantify the influence of these parameters on heat transfer coefficients.