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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Further efforts to limit lunar internal temperatures from electrical conductivity determinations

A representative set of 18 electrical conductivity profiles consistent with lunar transfer function data in the 0.00001 to 0.001 Hz range is generated by means of a Monte Carlo procedure. Laboratory electrical conductivity vs temperature data extrapolated to lower and higher temperatures are used to convert the 18 profiles into temperature profiles for a series of radially homogeneous olivine-pyroxene mixtures ranging from 100% olivine to 100% aluminous orthopyroxene. The former yields selenotherms approaching the Ringwood-Essene solidus at depths near 500 km, while additions of the latter in concentrations greater than 15-30 vol% leads to cooler selenotherms which approach the solidus only at depths greater than about 1000 km. On the basis of independent geophysical constraints, the profile envelopes for compositions containing more than 15-30 vol% aluminous orthopyroxene are considered to be the most probable.

Hood, L. L.↗
DOE OSTI2026

Modeling the Potential Impact of Storage on the US Power Sector in a Multisector Dynamic Context

The electric power sector is expected to grow in size, importance, and complexity around the world as economies expand and electric supply technologies and demand patterns evolve in significant ways. At the same time the electric power sector may see substantial increases in VRE generating technologies which can add variability and uncertainty to the diurnal and seasonal profile of electric supply. With these forces in play, the emergence of modular, flexible electricity storage technologies may have profound impacts on the operation of electric power systems. Here we reduce a storage modeling gap in MSD models by incorporating grid-based electricity storage into the electric sector dynamics of the Global Change Analysis Model-USA (GCAM-USA) with improved power sector representation. We find a potentially significant role for storage technologies in the future of the U.S. power system, with storage capacity ranging from 7.7 to 14.7 GW in 2050 and 13.9 to 31.6 GW in 2100 across several techno-economic scenarios. We also find that storage can help to smooth variability in residual load arising from evolving electricity demands and the introduction of high shares of VRE to the electric grid. This reduces reliance on high-cost peaking generators, improves system-wide capacity factors, and limits curtailment of VRE.

Patel, Pralit L.↗
NASA NTRS1985

Atmospheric electric field and current configurations in the vicinity of mountains

A number of investigations have been conducted regarding the electrical distortion produced by the earth's orography. Hays and Roble (1979) utilized their global model of atmospheric electricity to study the effect of large-scale orographic features on the currents and fields of the global circuit. The present paper is concerned with an extension of the previous work, taking into account an application of model calculations to orographic features with different configurations and an examination of the electric mapping of these features to ionospheric heights. A two-dimensional quasi-static numerical model of atmospheric electricity is employed. The model contains a detailed electrical conductivity profile. The model region extends from the surface to 100 km and includes the equalization layer located above approximately 70 km. The obtained results show that the electric field and current configurations above mountains depend upon the curvature of the mountain slopes, on the width of the mountain, and on the columnar resistance above the mountain (or mountain height).

Tzur, I.↗
NASA NTRS2016

Applications of Electrified Dust and Dust Devil Electrodynamics to Martian Atmospheric Electricity

Atmospheric transport and suspension of dust frequently brings electrification, which may be substantial. Electric fields of 10 kV m(exp. -1) to 100 kV m(exp. -1) have been observed at the surface beneath suspended dust in the terrestrial atmosphere, and some electrification has been observed to persist in dust at levels to 5 km, as well as in volcanic plumes. The interaction between individual particles which causes the electrification is incompletely understood, and multiple processes are thought to be acting. A variation in particle charge with particle size, and the effect of gravitational separation explains to, some extent, the charge structures observed in terrestrial dust storms. More extensive flow-based modelling demonstrates that bulk electric fields in excess of 10 kV m(exp. -1) can be obtained rapidly (in less than 10 s) from rotating dust systems (dust devils) and that terrestrial breakdown fields can be obtained. Modelled profiles of electrical conductivity in the Martian atmosphere suggest the possibility of dust electrification, and dust devils have been suggested as a mechanism of charge separation able to maintain current flow between one region of the atmosphere and another, through a global circuit. Fundamental new understanding of Martian atmospheric electricity will result from the ExoMars mission, which carries the DREAMS (Dust characterization, Risk Assessment, and Environment Analyser on the Martian Surface) MicroARES (Atmospheric Radiation and Electricity Sensor) Instrumentation to Mars in 2016 for the first in situ electrical measurements.

Lighting Discharge↗
DOE OSTI2025

Commercial building HVAC demand flexibility with model predictive control: Field demonstration and literature insights

Model Predictive Control (MPC) for building Heating Ventilation and Air Conditioning (HVAC) systems is beginning to gain traction in the market, with a few controls companies incorporating it into their product offerings. However, it remains difficult to assess whether the energy cost savings are enough to justify the cost of MPC implementation for a particular building, given the limited number of reported demonstrations. For small commercial and residential buildings with relatively uniform systems, standardized approaches can help lower implementation costs. In contrast, for large buildings or district systems, the potential magnitude of cost savings could justify more customized solutions. Estimating the cost-effectiveness of MPC becomes more challenging for medium and large commercial buildings, where a one-size-fits-all solution may not be suitable, and the potential energy cost savings may be insufficient to justify a customized solution. To make MPC technology more appealing, incorporating additional value streams beyond energy efficiency alone can significantly increase its attractiveness. One such revenue stream is demand flexibility, in response to dynamic electricity prices, where MPC can leverage the thermal mass of the building to shift the load and support the grid. Building on an extensive literature review of MPC field studies focused on cost savings and demand flexibility, this paper presents the results of implementing MPC control in a large office building HVAC system in Berkeley, CA. Four different dynamic electricity price profiles were integrated into the MPC objective function to shift building demand while maintaining comfort, and field testing was performed with each price profile across four seasons. The results show potential for 40–65 % demand decrease percentage and up to 61 % annual cost savings compared to the existing rule-based control strategy, under the tested dynamic price scenarios. This paper also presents a sensitivity analysis on the cost savings with respect to the price profile variability, discusses the implementation effort for the price-responsive MPC, and compares the cost savings found in this study to those found in literature on the basis of dynamic price variability, or so-called Electricity Price Relative Standard Deviation.

Zanetti, Ettore↗
NASA NTRS1977

Spectroscopic determination of electrical conductivity in an MHD duct from absolute intensity measurements

Measurements of the electrical conductivity in the NASA Lewis cesium seeded, H2-O2 MHD duct have been previously reported. In order to corroborate the above measurements and to analyze the possibility of nonuniform seed injection as a cause of the deviations, a spectroscopic investigation of the plasma conductivity has been undertaken. Transverse profiles of the absolute integrated intensity were measured from the optically thin lines of CSI-.5664 microns and .5636 microns. Radial profiles of emission coefficient were obtained from the measured transverse profiles of intensity by Abel inversion. Radial profiles of electrical conductivity were then obtained under two different assumptions. In the first, the Cs seed fraction is assumed uniform and equal to the measured flow rate at the time when the temperature and conductivity were obtained. In the second method, the local temperature and pressure are taken to be those given by a one-dimensional channel calculation including heat transfer and friction. In this case profiles of conductivity and seed fractions are obtained. The results of the two methods are compared to the previously measured conductivity.

Wang, S. Y.↗
DOE OSTI2025

Grid-responsive hydrogen production: Capital utilization and current density vs. efficiency in variable electricity markets

To achieve low-cost hydrogen production from water electrolyzers, grid tied electrolysis may need to operate dynamically to minimize the cost of supplying energy to the electrolyzer stack and produce hydrogen during low-cost hours and turn off/down during high-cost hours. Operating systems in this way can decrease capital utilization (capacity factor) and electricity costs. This strategy would shift the dominant cost drivers away from electricity (and thus efficiency) to the capital costs of the system, due to the underutilized capital when operating at low-capacity factors. Increasing the operational current density of the system could, in effect, reduce the capital cost of the system while producing hydrogen at a lower efficiency on a per unit energy basis. In the variable electricity cost profiles analyzed in this paper, increasing the current density for liquid alkaline from 0.5 A/cm2 to 1.5 Ac/m2 and proton exchange membrane electrolyzers from 2 A/cm2 to 4 A/cm2 resulted in substantial reductions in the levelized cost of hydrogen. Additionally, as capacity factors and electricity costs decrease, the optimal operating current density of the electrolyzer systems analyzed increases. These findings suggest R&D efforts should focus on increasing the operational current densities, reducing the turn down ratios, and understanding the durability implications of those strategies on low-temperature liquid alkaline and proton exchange membrane electrolyzers.

08 HYDROGEN↗
NASA NTRS1976

Voltage profile program for the Kennedy Space Center electric power distribution system

The Kennedy Space Center voltage profile program computes voltages at all busses greater than 1 Kv in the network under various conditions of load. The computation is based upon power flow principles and utilizes a Newton-Raphson iterative load flow algorithm. Power flow conditions throughout the network are also provided. The computer program is designed for both steady state and transient operation. In the steady state mode, automatic tap changing of primary distribution transformers is incorporated. Under transient conditions, such as motor starts etc., it is assumed that tap changing is not accomplished so that transformer secondary voltage is allowed to sag.

Source record↗
DOE OSTI2026

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Mileski, Matthew [AFIT]↗
NASA NTRS2010

Combined Aircraft and Satellite-Derived Storm Electric Current and Lightning Rates Measurements and Implications for the Global Electric Circuit

Using rotating vane electric field mills and Gerdien capacitors, we measured the electric field profile and conductivity during 850 overflights of electrified shower clouds and thunderstorms spanning regions including the Southeastern United States, the Western Atlantic Ocean, the Gulf of Mexico, Central America and adjacent oceans, Central Brazil, and the South Pacific. The overflights include storms over land and ocean, with and without lightning, and with positive and negative fields above the storms. The measurements were made with the NASA ER-2 and the Altus-II high altitude aircrafts. Peak electric fields, with lightning transients removed, ranged from -1.0 kV/m to 16 kV/m, with a mean value of 0.9 kV/m. The median peak field was 0.29 kV/m. Integrating our electric field and conductivity data, we determined total conduction currents and flash rates for each overpass. With knowledge of the storm location (land or ocean) and type (with or without lightning), we determine the mean currents by location and type. The mean current for ocean storms with lightning is 1.6 A while the mean current for land storms with lightning is 1.0 A. The mean current for oceanic storms without lightning (i.e., electrified shower clouds) is 0.39 A and the mean current for land storms without lightning is 0.13 A. Thus, on average, land storms with or without lightning have about half the mean current as their corresponding oceanic storm counterparts. Over three-quarters (78%) of the land storms had detectable lightning, while less than half (43%) of the oceanic storms had lightning. We did not find any significant regional or latitudinal based patterns in our total conduction currents. By combining the aircraft derived storm currents and flash rates with diurnal lightning statistics derived from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) low Earth orbiting satellites, we reproduce the diurnal variation in the global electric circuit (i.e., the Carnegie curve) to within 4% for all but two short periods of time. This excellent agreement with the Carnegie curve was obtained without any tuning or adjustment of the satellite or aircraft data. Given our data and assumptions, mean contributions to the global electric circuit are 0.7 kA (ocean) and 1.1 kA (land) from lightning-producing storms, and 0.22 kA (ocean) and 0.04 (land) from electrified shower clouds, resulting in a mean total conduction current estimate for the global electric circuit of 2.0 kA. Breaking the results down into mean storm counts reveals 1100 for land storms with lightning, 530 for ocean storms without lightning, 390 for ocean storms with lightning, and 330 for land storms without lightning.

Mach, Douglas M.↗
NASA NTRS2011

Global Electric Circuit Implications of Combined Aircraft Storm Electric Current Measurements and Satellite-Based Diurnal Lightning Statistics

Using rotating vane electric field mills and Gerdien capacitors, we measured the electric field profile and conductivity during 850 overflights of thunderstorms and electrified shower clouds (ESCs) spanning regions including the Southeastern United States, the Western Atlantic Ocean, the Gulf of Mexico, Central America and adjacent oceans, Central Brazil, and the South Pacific. The overflights include storms over land and ocean, and with positive and negative fields above the storms. Over three-quarters (78%) of the land storms had detectable lightning, while less than half (43%) of the oceanic storms had lightning. Integrating our electric field and conductivity data, we determined total conduction currents and flash rates for each overpass. With knowledge of the storm location (land or ocean) and type (with or without lightning), we determine the mean currents by location and type. The mean current for ocean thunderstorms is 1.7 A while the mean current for land thunderstorms is 1.0 A. The mean current for ocean ESCs 0.41 A and the mean current for land ESCs is 0.13 A. We did not find any significant regional or latitudinal based patterns in our total conduction currents. By combining the aircraft derived storm currents and flash rates with diurnal flash rate statistics derived from the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) low Earth orbiting satellites, we reproduce the diurnal variation in the global electric circuit (i.e., the Carnegie curve) to within 4% for all but two short periods of time. The agreement with the Carnegie curve was obtained without any tuning or adjustment of the satellite or aircraft data. Given our data and assumptions, mean contributions to the global electric circuit are 1.1 kA (land) and 0.7 kA (ocean) from thunderstorms, and 0.22 kA (ocean) and 0.04 (land) from ESCs, resulting in a mean total conduction current estimate for the global electric circuit of 2.0 kA. Mean storm counts are 1100 for land thunderstorms, 530 for ocean ESCs, 390 for ocean thunderstorms, and 330 for land ESCs.

Mach, Douglas M.↗
DOE OSTI2026

Preliminary Assessment for the Electric Load Shifting Potential of Integrating Thermal Energy Storage with Heat Pumps in Residential Buildings in Texas of United States

The widespread adoption of electric-driven heat pumps for heating and cooling is expected to significantly increase electric demand. Cooling electric demand will result in an increase in peak hours electric loads, placing additional strain on the grid during peak hours. This challenge, combined with the current rapidly increasing demand from data centers, exacerbates the electric demand duck curve problem. Integrating thermal energy storage (TES) with heat pump can shift electric use for heating and cooling from peak to off-peak hours of the electric grid, which can help flatten the daily electric demand profile of a building. This paper presents a novel design that integrates heat pump with TES (HP-TES), which uses phase change materials (PCM). Heat pump charges TES by melting or freezing PCM during off-peak hours when there is no thermal demand from the building. TES is then discharged (i.e., by freezing or melting PCM) during peak hours to provide a more favorable heat source or heat sink for the heat pump to meet the thermal demand of the building with lower electricity use than conventional air-source heat pumps. Computer simulations were developed to predict the performance of HP-TES applied to a typical single-family house in the US. The building-level simulation results were scaled up to preliminarily assess the aggregated impacts of deploying HP-TES across all single-family houses in Texas of the United States, including reduction of peak demand of the electric grid.

Anees, Fady [ORNL]↗
NASA NTRS1977

Spectroscopic determination of electrical conductivity in an MHD duct from absolute intensity measurements

Measurements of the electrical conductivity in the NASA Lewis cesium seeded, H2-O2 MHD duct made by applying a voltage across the channel from one end electrode to the other, measuring the current, and using the inner electrodes as probes to monitor the voltage distribution along the channel were found to be in good agreement with theory except at low combustion pressures and/or high ratios of seed/oxygen mass flows. To corroborate these measurements and to analyze the possibility of nonuniform seed injection as a cause of the above deviations, a spectroscopic investigation of the plasma conductivity was undertaken. Radial profiles of emission coefficient were obtained from measured transverse profiles of the absolute integrated intensity by Abel inversion. Radial profiles of electrical conductivity were then obtained under two different assumptions. In the first the Cs seed fraction is assumed uniform and equal to the measured flow rate at the time when the temperature and conductivity were obtained. In the second method the local temperature and pressure are taken to be those given by a one-dimensional channel calculation including heat transfer and friction. The results of the two methods are compared to the previously measured conductivity.

Wang, S. Y.↗
DOE OSTI2025

CdSeTe solar-cell performance with different dopant types

Four doping conditions were explored for CdTe-based solar cells: p-type As and P, n-type Al, and no intentional doping. In each case, the CdTe absorber was alloyed with Se, but only near the normal front-side, light entry for the cells, while the dopants were added from the back. Cells with p-dopants showed efficiencies up to 20% with front-side illumination, but the n-doped and undoped ones were close to zero. With back-side illumination, this was reversed with undoped up to 8% and p-doped ones only about 2%. These results are explained by Kelvin-probe measurements of electric-field profiles, which showed that the diode field was near the front for the higher-efficiency p-doping, but near the back for undoped and n-doped.

14 SOLAR ENERGY↗
NASA NTRS2010

Kennedy Space Center's Partnership with Graftel Incorporated

NASA Kennedy Space Center (KSC) has recently partnered with Graftel Incorporated under an exclusive license agreement for the manufacture and sale of the Smart Current Signature Sensor. The Smart Current Signature Sensor and software were designed and developed to be utilized on any application using solenoid valves. The system monitors the electrical and mechanical health of solenoids by comparing the electrical current profile of each solenoid actuation to a typical current profile and reporting deviation from its learned behavior. The objective of this partnership with Graftel is for them to develop the technology into a hand-held testing device for their customer base in the Nuclear Power Industry. The device will be used to perform diagnostic testing on electromechanical valves used in Nuclear Power plants. Initially, Graftel plans to have working units within the first year of license in order to show customers and allow them to put purchase requests into their next year's budget. The subject technology under discussion was commercialized by the Kennedy Space Center Technology Programs and Partnerships Office, which patented the technology and licensed it to Graftel, Inc., a company providing support, instrumentation, and calibration services to the nuclear community and private sector for over 10 years. For the nuclear power industry, Graftel designs, manufacturers, and calibrates a full line of testing instrumentation. Grafters smart sensors have been in use in the United States since 1993 and have proved to decrease set-up time and test durations. The project was funded by Non-Destructive Engineering, and it is felt that this technology will have more emphasis on future vehicles. Graftel plans to market the Current Signature Sensor to the Electric Utility industry. Graftel currently supplies product and services to the Nuclear Power Industry in the United States as well as internationally. Product and services sold are used in non-destructive testing for valves, penetrations and other applications. Graftel also supplies testing services to an industrial customer base. The customer base includes 90 percent of the U.S. Nuclear plants and plants in Brazil, Europe, and Asia. Graftel works internationally with two representative groups and employees and has ten people at the principle location and a group of contract engineers around the country.

Dunn, Carol Anne↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Thermal Dynamics and Lithium Plating Detection in High‐Power Li‐Ion Batteries for eVTOL Applications

The rapid electrification of aerial transportation is driving the need for high‐performance Li‐ion batteries that can operate reliably under stringent thermal and safety constraints. The unique mission profile of electric Vertical Take‐off and Landing (eVTOL) aircraft necessitates a focused investigation into the thermal behavior and safety characteristics of these batteries. Here, in this study, operando isothermal microcalorimetry is employed to examine the thermal evolution of Li‐ion batteries under cycling conditions representative of eVTOL operations. These findings reveal that high‐power discharge events—such as those during take‐off and landing—shift the thermal response toward exothermic behavior, in contrast to the typically endothermic response expected under near‐equilibrium cycling conditions. Additionally, the results suggest that advanced electrolyte formulations may help suppress excess heat generation, thereby improving battery safety. Notably, the calorimetric results exhibit a distinct thermal signature associated with lithium plating, offering a potential diagnostic for detecting Li plating during eVTOL operation. Overall, this study demonstrates the utility of isothermal microcalorimetry as a valuable tool for assessing thermal risks in Li‐ion batteries for eVTOL applications, and highlights the importance of targeted design strategies to mitigate safety hazards during high‐power demand scenarios.

Li plating↗