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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Performance of Different Water-Based Binder Formulations for Ni-Rich Cathodes Evaluated in LiNi 0.8 Mn 0.1 Co 0.1 O 2 //Graphite Pouch Cells

Water-based processing for lithium-ion battery electrodes is attractive due to its lower manufacturing cost and smaller environmental impact. However, multiple challenges associated with aqueous cathode processing have hindered commercial adoption. Polymer binders are an important component of the electrode, and thus the choice of binders can alter electrode cycling performance significantly. In this work, four different water-based binder combinations are investigated for Ni-rich LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)-based cathodes, with a focus on the long-term electrochemical performance in practical-format full pouch cells. No additional pH-modulating additives were added to the aqueous cathode slurries, and no protective coatings were present on the cathode or aluminum current collector. Results are compared with the standard PVDF/NMP-based binder/solvent combination, used as a baseline. The influence of water-based binder type on slurry rheology and electrode microstructure are also discussed. All cells made by water-processing had worse rate performance compared to the baseline. However, the cell discharge capacity after 1000 U.S. Advanced Battery Consortium (USABC) cycles at C/3 charge/discharge rate was comparable to the baseline for two of the water-based cathode formulations (CMC & JSR, and LiPAA), demonstrating the potential viability of aqueous-processed Ni-rich cathodes at a commercial scale.

25 ENERGY STORAGE↗
DOE OSTI2022

Electrolyzer Performance Loss from Accelerated Stress Tests and Corresponding Changes to Catalyst Layers and Interfaces

Stress tests are developed for proton exchange membrane electrolyzers that utilize low catalyst loading, elevated potential, and frequent cycling with square- and triangle-waves to accelerate anode catalyst layer degradation during intermittent operation. Kinetics drive performance losses (ohmic/transport secondary) and are accompanied by decreasing exchange current density, decreasing cyclic voltammetric capacitance, and increasing polarization resistance. Decreased kinetics are likely due to a combination of iridium (Ir) migration into electrochemically inaccessible locations in the anode or membrane, Ir particle growth (supported by X-ray scattering), changes in the extent of the Ir oxidation state (supported by X-ray absorption spectroscopy), and anode catalyst layer reordering. Decreasing catalyst/transport layer contact and catalyst/membrane interfacial tearing may add contact resistances and account for increasing ohmic losses. Performance losses for low and moderate catalyst loading, as well as from accelerated and model wind/solar cycling protocols, were likewise dominated by kinetics but vary in severity. Additionally, accelerated cycling (1 cycle per minute) appears to reasonably accelerate relevant loss mechanisms and can be used to project electrolyzer lifetime from anode deterioration. Ongoing accelerated stress test development and studies into performance loss mechanisms will continue to be critical as electrolysis shifts to intermittent power and low-cost applications.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Performance of a Novel In-Situ Converted Additive for High Voltage Li-ion Pouch Cells

In search for new classes of additives for high voltage NMC/graphite lithium-ion cells, the precursor additive bis(trimethylsilyl) malonate (bTMSM) is shown to be activated via a spontaneous reaction with LiPF 6 and LiBF 4 salts in carbonate-based electrolyte to form lithium tetrafluoro(malonato)phosphate (LiTFMP), and lithium difluoro(malonato)borate (LiDFMB), respectively. The reaction schemes and rates were studied via NMR spectroscopy and GCMS. The effects of LiTFMP and LiDFMB on high voltage electrochemical performance were then examined up to 4.5 V in Li[Ni 0.4 Mn 0.4 Co 0.16 ]O 2 (NMC442)/graphite and Li[Ni 0.6 Mn 0.4 Co 0.0 ]O 2 (NMC/640)/graphite pouch cells using aggressive voltage-hold cycling, long-term charge/discharge cycling, storage experiments, electrochemical impedance spectroscopy, and gas evolution measurements. While in situ converted additives suffer from gassing issues due to the presence of trimethylfluorosilane (TMSF) gas, a side product of the in situ reaction of bTMSM with LiPF 6 , the cycling and storage capability for the activated additives under study shows competitive performance and controlled impedance when compared to other well-known high voltage additives. Micro X-ray fluorescence spectroscopy (μXRF) confirmed that LiTFMP successfully minimizes the rate of transition metal deposition on the surface of graphite apparently by forming a protective agent at the cathode surface, hence allowing for improved cycling performance at high voltages.

25 ENERGY STORAGE↗
DOE OSTI2023

Elucidating the Effects of Temperature on Nonaqueous Redox Flow Cell Cycling Performance

The impact of cell temperature is a relatively underexplored area within the burgeoning field of nonaqueous redox flow batteries (NAqRFBs). Here, we investigate the effect of elevated temperature on the performance of nonaqueous redox electrolytes and associated flow cells. Using a model compound, N-(2-(2-methoxyethoxy)-ethyl)phenothiazine (MEEPT), in a propylene-carbonate-based electrolyte, we experimentally measure the temperature dependence of relevant physicochemical properties (i.e., electrolyte conductivity, viscosity, diffusivity) and electrochemical characteristics (i.e., chemical and electrochemical reversibility) across a temperature range of 30 °C to 70 °C. We then perform flow cell studies, finding that while ohmic and mass transport resistances decrease significantly with increases in temperature for the MEEPT/MEEPT +· redox couple, accessible electrolyte capacity gradually reduces at temperatures > 50 °C. Ex-situ, post-test characterization using microelectrode voltammetry suggests that this capacity fade is due to instability of the MEEPT radical cation. Finally, using MEEPT as a posolyte and a model viologen negolyte (bis(2-(2-methoxyethoxy)ethyl)viologen), we assemble a full cell and perform polarization analyses, observing a 2× increase in the peak power density when the operating temperature is increased from 30 °C to 70 °C. Broadly, this work highlights opportunities for systematic engineering of nonaqueous electrolytes and flow cells for higher power operation at elevated temperatures.

Electrochemistry↗
DOE OSTI2024

Theoretical Understanding of Effects of Operating Modes on the Performance Durability of Solid Oxide Cells: A Comparison between Potentiostatic and Galvanostatic Operations

In solid oxide cells (SOCs), the choice between galvanostatic (constant current) and potentiostatic (constant voltage) modes does not significantly affect the performance of SOCs as long as the cell components remain unchanged and intact. However, the degradation of cell components, which leads to changes in electrochemical and physical properties of the cell, elevates the importance of the selected operating mode. This paper aims to investigate the effects of galvanostatic and potentiostatic operating modes on the evolving properties of SOCs and their subsequent influence on performance durability. Employing non-equilibrium thermodynamic analysis, a crucial approach for understanding the degradation phenomena within an active electrochemical system, this study aims to provide in-depth insights into how these operating modes affect the longevity and efficacy of SOCs. Key findings include: In cases where oxygen electrode (OE) degradation is accelerated by higher partial pressure of oxygen ( p O 2 ), operating under constant voltage electrolysis can mitigate the high p O 2 at the OE|electrolyte (OE|EL) interface. Conversely, if OE degradation occurs more rapidly under a lower p O 2 , constant current electrolysis is more effective in suppressing degradation by achieving a high p O 2 at the OE|EL interface. For degradation of the fuel electrode (FE) due to higher p O 2 , constant current electrolysis is beneficial for more stable performance, which helps maintain low p O 2 at the FE|EL interface. When FE degradation is accelerated by lower p O 2 , constant voltage electrolysis can avert low p O 2 at the FE|EL interface. In practical scenarios, more complex degradation mechanisms come into play, especially when p O 2 significantly deviates from initial conditions. Degradation in one electrode can influence p O 2 in the other electrode, a phenomenon more pronounced in potentiostatic than in galvanostatic electrolysis.

Electrochemistry↗
DOE OSTI2024

Correlation Between Microscopic Current Fluctuations Observed at Ultra-Microelectrodes and Macroscopic Bulk Electrolysis Performance in Redox-Active Microemulsions

Microemulsions (μEs) have been proposed as redox flow battery (RFB) electrolytes that maximize ionic conductivity and charge capacity by synergizing two immiscible phases. However, charge transfer during electrolysis in μEs is poorly understood. Here, we show that ultramicroelectrode electrolysis of ferrocene-loaded μEs –20%, 60%, and 90% water - reveals stochastic current fluctuations. These are differentiated in the scanning electrochemical microscopy (SECM) geometry, where power spectral density analysis showed distinct changes in the frequency contributions. SECM in the substrate generation-tip collection mode showed that fluctuations arise under mass-transfer control. Significant differences in the diffusion coefficient of ferrocene species were deducted from SECM approach curves, suggesting phase transfer behavior. Using bulk electrolysis, we calculated the charge accessibility and cycling behavior in the μEs. A decrease in the stochastic behavior of the μEs seems to correlate to a higher accessibility and cycling performance, with the 90% water μE displaying the best reversibility and the 60% the lowest. Altogether, these results suggest that Marangoni-type convection driven by concentration gradients and/or μE restructuring during charge transfer play a role in the electrochemical performance of μEs. This presents opportunities for screening and diagnosing the performance of these emerging RFB electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

Impact of Module Configuration on Lithium-Ion Battery Performance and Degradation: Part I. Energy Throughput, Voltage Spread, and Current Distribution

Batteries are commonly connected in series and parallel to create modules that fulfill the power and energy requirements of specific applications. However, conclusions about battery performance and degradation under different conditions, as well as predictive models, are often derived from single cell cycling results. In this study, we evaluate the performance of six different series-parallel configurations of commercial lithium nickel manganese cobalt cells over hundreds of cycles. Each cell within the modules was individually instrumented for voltage, current, and temperature monitoring. We quantified the impact of module configuration on overall energy throughput, the voltage spread among series-connected cells, and the current heterogeneity in parallel-connected cells. This module cycling study, one of the broadest reported to date, supports systematic evaluation of the performance trade-offs, pack penalty, and safety implications of different module configurations.

25 ENERGY STORAGE↗
DOE OSTI2025

Effect of Carbon Support Heat Treatment on the Performance of Ion-Pair High-Temperature PEM Fuel Cells

Heat treatment can significantly alter the physical and chemical properties of carbon supports, thereby influencing the performance of proton exchange membrane (PEM) fuel cells. This study explores how varying carbon heat treatment temperatures—from 1,000 °C to 2,200 °C—affect the structure and performance of platinum-based catalysts in high-temperature PEM fuel cells. Increasing the heat treatment temperature led to a notable decrease in surface area, along with increases in carbon grain size and hydrophobicity. Although the catalyst supported on carbon treated at 1,000 °C exhibited the highest catalyst activity, the MEA using carbon treated at 1,500 °C delivered the best overall fuel cell performance. This is attributed to an optimized balance between hydrophobicity and accessible surface area, which enhances water management and catalyst utilization. These findings underscore the importance of carbon support engineering in improving the efficiency of ion-pair high-temperature PEM fuel cells.

08 HYDROGEN↗
DOE OSTI2026

Cobalt-Free Lithium and Manganese Rich Cathodes for Electric Vehicle Applications: Influence of Inherent Properties on Temperature-Dependent Performance

Lithium- and manganese-rich (LMR) oxides are attractive options as next-generation, earth-abundant cathodes. Despite inherent properties that result in less-than-ideal performance, advancements in this class of cathodes toward commercialization have been steady. Herein, we report a benchmark study on the influence these inherent properties exert on the discharge rate of Co-free, LMR//graphite (Gr) cells as a function of temperature. Because low-temperature performance is an essential consideration in the design of electric vehicle cells, understanding the limitations of current LMR systems is essential in understanding their current applicability as well as strategies for improvement. This work identifies two major limitations in the rate performance of LMR//Gr cells and suggests several areas of focus where practical strategies may provide improvements.

25 ENERGY STORAGE↗
DOE OSTI2021

Carbon Corrosion in Polymer Electrolyte Fuel Cells: A Complex Interplay between Morphological Changes and Electrochemical Performance

Due to the high gravimetric energy density of hydrogen, the focus of implementation of polymer electrolyte fuel cells (PEFCs) has shifted from light duty passenger vehicles to heavy duty vehicles such as buses, trucks, locomotives and marine vessels. A mechanistic understanding of degradation is therefore necessary to improve durability and efficiency. During start-up and shut-down (SUSD) of PEFC systems, the catalyst (Pt nanoparticles embedded on carbon support) undergoes local potentials ~ 1 - 1.5 V caused by a combination of fuel (H 2 ) starvation, mixed fuel region and cell reversal. This leads to a series of degradation phenomenon including reduction in cathode catalyst layer (cCL) thickness and porosity, loss in electrochemical surface area (ECSA), ionomer degradation and loss in electrical contact, therefore resulting in severe performance loss. The convoluted relationship between these individual degradation mechanisms, their chronology and their effects on electrochemical performance are yet unresolved. Here, the complex interplay between morphological changes due to carbon corrosion and its effects on the electrochemical performance were analyzed using a combination of detailed electrochemical characterization, spectroscopy, and electron microscopy techniques.

25 ENERGY STORAGE↗
DOE OSTI2021

High Performance Approximate Computing

This code repository contains the implementation of the "High-Performance Approximate Computing" (HPAC) toolkit. The toolkit allows you to approximate your own C/C++. The developer uses "pragma's" to annotate code regions as approximate. The compiler extensions lower these pragmas to either compiletime approximate techniques or runtime approximation techniques. At execution time, the implemented runtime system decides which annotated regions it should approximate. HPAC also provides a set of script utilities. The utilities perform a grid search within approximation parameters and performance. The user can analyze the raw data to identify optimal approximation techniques for the application.

Parasyris, Konstantinos↗
DOE OSTI2022

Anomalous And Normal High Performance Computing Datacenter Activities

This code provides annotation and an interface to 10+ hours of video activities in a high performance datacenter with 20+ different types of anomalous activities. The purpose is to enable machine learning for video surveillance systems in high performance computing centers. This is the first code of this type addressing the space of high performance computing datacenters.

Anderson, Matthew↗
DOE OSTI2023

High-Performance GMRES Mixed-Precision (HPG-MxP) Benchmark

SAND2024-08539O The High Performance GMRES Mixed-Precision (HPG-MxP) is a benchmark for ranking high-performance supercomputers, allowing use of mixed-precision. Similar to HPCG benchmark, it is designed to profile the computers' capabilities to perform the computational and communication tasks that are commonly found in important classes of real-world applications. At the same time, like HPL-MxP benchmark, it allows the use of mixed-precision arithmetic, while ensuring the double-precision accuracy of the computed solution. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2026

NCCS High Performance GMRES Mixed Precision

HPG-MxP is a software package that performs a fixed number of multigrid preconditioned (using a Gauss-Seidel smoother) Generalized minimal residual (PGMRES) iterations in order to solve a possibly nonsymmetric large sparse linear system of equations. It is designed to be a benchmark to measure a computer's performance for sparse linear algebra workloads typical in scientific computing while allowing the use of mixed precision methods. The solution is required to have convergence characteristics and accuracy similar to double precision GMRES. It is based on the High Performance Conjugate Gradient Benchmark (HPCG) which restricts all implementations to use only the IEEE double precision format (FP64). The original implementation (https://github.com/hpg-mxp/hpg-mxp) was written by Ichitaro Yamazaki, Jennifer Loe, Christian Glusa, Sivasankaran Rajamanickam, Piotr Luszczek, and Jack Dongarra. Please refer to that repository for documentation on the original implementation. This version is maintained by the National Center for Computational Sciences at Oak Ridge National Laboratory. It is highly scalable and optimized for Oak Ridge Leadership Computing Facility (OLCF) systems, particularly Frontier.

Kashi, Aditya [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2026

Battery Performance and Cost Model (BatPaC) Version 6.0

SF-26-016 The Battery Performance and Cost model (BatPaC) is a calculation method based on Microsoft Excel spreadsheets that has been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEVs) and pure electrics. BatPaC was first developed in 2007, was subsequently peer reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. BatPaC has been updated and re-released multiple times since its original public release in 2011. This current version is BatPaC 6.0, which contains additional functionality needed to handle advances in automotive batteries, like the use of lithium metal and silicon anodes and the need to accommodate cell expansion and apply high levels of pressure.

KNEHR, KEVIN [Argonne National Laboratory (ANL), A↗
DOE OSTI2022

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗
DOE OSTI2022

Compressed basis GMRES on high-performance graphics processing units

Krylov methods provide a fast and highly parallel numerical tool for the iterative solution of many large-scale sparse linear systems. To a large extent, the performance of practical realizations of these methods is constrained by the communication bandwidth in current computer architectures, motivating the investigation of sophisticated techniques to avoid, reduce, and/or hide the message-passing costs (in distributed platforms) and the memory accesses (in all architectures). This article leverages Ginkgo’s memory accessor in order to integrate a communication-reduction strategy into the (Krylov) GMRES solver that decouples the storage format (i.e., the data representation in memory) of the orthogonal basis from the arithmetic precision that is employed during the operations with that basis. Given that the execution time of the GMRES solver is largely determined by the memory accesses, the cost of the datatype transforms can be mostly hidden, resulting in the acceleration of the iterative step via a decrease in the volume of bits being retrieved from memory. Together with the special properties of the orthonormal basis (whose elements are all bounded by 1), this paves the road toward the aggressive customization of the storage format, which includes some floating-point as well as fixed-point formats with mild impact on the convergence of the iterative process. We develop a high-performance implementation of the “compressed basis GMRES” solver in the Ginkgo sparse linear algebra library using a large set of test problems from the SuiteSparse Matrix Collection. We demonstrate robustness and performance advantages on a modern NVIDIA V100 graphics processing unit (GPU) of up to 50% over the standard GMRES solver that stores all data in IEEE double-precision.

97 MATHEMATICS AND COMPUTING↗