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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Human-in-the-Loop Motion Control of a Two-DOF Hydraulic Backhoe Powered by the Hybrid Hydraulic Electric Architecture (HHEA)

Abstract The Hybrid Hydraulic-Electric Architecture (HHEA) combines the respective power density and control advantages of hydraulic and electric actuation to save energy for off-road vehicles. It uses a set of selectable common pressure rails to transmit the majority of power and electric actuation to modulate that power. As it is critical that off-road vehicles can perform tasks dexterously and exactly as commanded by the operator, the switchings between discrete pressure rails pose a potential challenge for smooth and precise motion. A control strategy consisting of a backstepping nominal control and least norm transition control has previously been developed to address this issue. It has been tested on 1 degree of freedom (DOF) testbeds where known trajectories were able to be tracked precisely. This paper presents the implementation of the HHEA motion control strategy on a 2-DOF backhoe operated by a human operator via a 2-DOF joystick. Unlike previous studies, the duty cycle is unknown beforehand and the decision to change pressure rails is taken in real-time. The efficacy of the motion control strategy has been validated experimentally. Several strategies to improve the user interface: control in workspace coordinates, pressure feedback, and velocity field-based task specification, have also been implemented and demonstrated to make operating the multiple DOF, HHEA actuated machine more intuitive to novice operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Electric and Hydraulic Propel Torque Modulation for a Compact Track Loader With the Hybrid Hydraulic Electric Architecture (HHEA)

Abstract The Hybrid Hydraulic Electric Architecture (HHEA) has previously been proposed for off-highway vehicles to reap the efficiency and controllability benefits of electrification without needing very large electric motors. This is achieved with the use of a set of selectable common pressure rails to transmit the majority of power and small electric motors to modulate that power. Previous work has shown significant energy savings for the work circuits of a variety of machines. In this paper, the energy saving potential of HHEA for the propel circuit of a compact track loader is studied. The ports of the track hydraulic motors are selectably connected to the common pressure rails, and instead of using the electric assist motors to buck/boost pressure, as in HHEA for linear actuators, small electric assist motors are used to add/subtract torque directly. The interplay between the torque limits of the electric motors and the ability of the hydraulic motor to vary displacements is studied, along with the effect these factors have on energy saving potential. It is found that the ability to vary the displacement of the hydraulic motor allows for: more efficient electric motor operating conditions, reduced electric torque requirement, and reduced pressure rail switching events. All three of these advantages can be achieved at once using variable displacements; but trade-offs exist between these advantages (i.e. improved efficiency can be achieved at the expense of a larger electric torque requirement). Overall, the HHEA can reduce energy consumption by ∼ 36% compared to the stock machine, depending on the hydraulic motor’s ability to vary displacements, and assuming the electric motor torque is limited to 20% of that required in a direct electrification scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗
DOE OSTI2023

Plasmonic hybrid core-shell (HyCoS) AgPt NP template hybridized with GQDs for SERS enhancement of 4-MBA and BT

Surface-enhanced Raman spectroscopy (SERS) is an attractive vibrational spectroscopic technique that can enable a non-destructive and ultra-sensitive detection down to the single-molecule level. Herein, a novel hybrid SERS platform is developed based on hybrid core-shell (HyCoS) AgPt nanoparticles (NPs) and graphene quantum dots (GQDs) for the enhancement of Raman vibration of 4-mercaptobenzoic acid (4-MBA) and benzenethiol (BT). The unique design of HyCoS AgPt NPs induces strong electromagnetic mechanism (EM) enhancement through the amplification of electromagnetic fields by the excitation of high-density surface plasmons and hot spots. Superior localized surface plasmon resonance (LSPR) is generated by the AgPt core-shell and background Ag NP coupling, which is systematically investigated by the optical properties and FDTD simulations. The background Ag NPs can further increase the coverage of metallic NPs, leading to higher-density hot spots and enhanced SERS response. At the same time, GQDs can provide plentiful accessible edges for the charge transfer to the HOMO and LUMO of 4-MBA and BT based on the chemical mechanism (CM) enhancement. The mixing approach of GQDs and target molecules on the HyCoS AgPt NPs can significantly amplify the Raman signals via the strong adsorption of probe molecules by the π–π interaction. The enhancement factors of proposed SERS platform can reach ~10 7 and ~10 5 for the 4-MBA and BT respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Hybrid and Inorganic Vacancy-Ordered Double Perovskites A 2 WCl 6

We report hybrid and all-inorganic, vacancy-ordered double perovskites of d 2 W 4+ with the formula A 2 WCl 6 (A = CH 3 NH 3 + , Rb + , and Cs + ). These compounds, which are reddish in color, can be distinguished from structurally similar compounds obtained by hydrothermal methods on the basis of structure, spectroscopic, and magnetic properties. The latter are green and incorporate oxygen, with the actual formula Cs 2 WO x Cl 6–x and distinct optical absorption and emission behavior. Furthermore, the local-moment magnetism of the pure-red d 2 compounds reported here does not correspond to the appropriate Kotani model, suggesting as-yet undiscovered physics in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces

Performing reliable computer simulations of elementary processes occurring at metal–water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Here, due to the large system sizes needed for studying processes at liquid water–metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate–metal interactions. Next, for the water–water and water–adsorbate interactions, we used established classical force fields. Finally, for the water–Ru surface interaction, for which no reliable force fields have been published, we used Behler–Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.

Adsorption↗
DOE OSTI2022

Ultralow-Loading Pt/Zn Hybrid Cluster in Zeolite HZSM-5 for Efficient Dehydroaromatization

Minimizing Pt loading without sacrificing catalytic performance is critical, particularly for designing cost-efficient hydrocarbon transformation catalysts. Here, we show that ultralow-loading (0.001–0.05 wt %) Pt- and Zn-functionalized HZSM-5 catalysts, prepared through simple ion exchange and impregnation, are highly active and stable for light alkane dehydroaromatization (DHA). The specific activity of benzene, toluene, and xylene is up to 8.2 mol/g Pt /min (or 1592 min –1 ) over the 0.001 wt % Pt–Zn 2 /HZSM-5 catalyst during ethane DHA at 550 °C under atmospheric pressure. Additionally, such bimetallic Pt x –Zn y /HZSM-5 catalysts are highly stable in contrast to the monometallic Pt/HZSM-5 catalysts. The rate constant of deactivation (k deactiv ), according to the first-order generalized power law equation model, for the bimetallic catalysts is up to 120 times lower than that of the monometallic counterparts, depending on the Pt loading. In conclusion, this breakthrough is achieved through the formation of the [Pt 1 –Zn n ] δ+ hybrid cluster, instead of Pt 0 cluster–proton adducts, in the micropores of the ZSM-5 zeolite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Battery-ultracapacitor hybrid energy storage system architecture for mild-hybrid power systems

A mild-hybrid energy storage system architecture is provided, comprising: a battery; an ultracapacitor connected in parallel with the battery; a passive battery pre-charge circuit connected between a terminal of the battery and a DC bus; a battery main contactor connected in parallel with the battery pre-charge circuit between the terminal of the battery and the DC bus; a passive ultracapacitor pre-charge circuit connected between a terminal of the ultracapacitor and the DC bus; an ultracapacitor main contactor connected in parallel with the ultracapacitor pre-charge circuit between the terminal of the ultracapacitor and the DC bus; and a control module configured to independently control operation of the battery pre-charge circuit, the battery main contactor, the ultracapacitor pre-charge circuit and the ultracapacitor main contactor.

Varigonda, Subbarao↗