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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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DOE OSTI2023

Electrification 2022 Annual Progress Report

The Electric Drive Technologies (EDT) program's mission is to conduct early-stage research and development on transportation electrification technologies that accelerate the development of cost-effective and compact electric traction drive systems that meet or exceed performance and reliability requirements of internal combustion engine (ICE)-based vehicles, thereby enabling electrification across all light-duty vehicle types. The Grid and Charging Infrastructure (G&I) program's mission is to conduct early-stage research and development on transportation electrification technologies that enable reduced petroleum consumption by light, medium, and heavy-duty vehicles. The program identifies and enables the role of vehicles in the future electrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Electrification (2023 Annual Progress Report)

This document summarizes the progress of VTO Electrification R&D projects supported during the fiscal year 2023. The Electric Drive Technologies (EDT) program’s mission is to conduct early-stage research and development on transportation electrification technologies that accelerate the development of cost-effective and compact electric traction drive systems that meet or exceed performance and reliability requirements of internal combustion engine (ICE)-based vehicles, thereby enabling electrification across all light-duty vehicle types. The Grid and Charging Infrastructure (G&I) program's mission is to conduct early-stage research and development on transportation electrification technologies that enable reduced petroleum consumption by light, medium, and heavy-duty vehicles. The program identifies and enables the role of vehicles in the future electrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating the Impacts of Autonomous Electric Vehicles Adoption on Vehicle Miles Traveled and CO2 Emissions

Autonomous electric vehicles (AEVs) can potentially revolutionize the transportation landscape, offering a safer, contact-free, easily accessible, and more eco-friendly mode of travel. Prior to the market uptake of AEVs, it is critical to understand the consumer segments that are most likely to adopt these vehicles. Beyond market adoption, it is also important to quantify the impact of AEVs on broader transportation systems and the environment, such as impacts on the annual vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions. In this pilot study, using survey data, a statistical model correlating AEV adoption intention and socioeconomic and built environment attributes was estimated, and a sensitivity analysis was conducted to understand the importance of factors impacting AEV adoption. We found that the market segments range from early adopters who are wealthy, technologically savvy, and relatively young to non-adopters who are more cautious to new technologies. This is followed by a synthetic population microsimulation of market penetration for the San Francisco Bay Area. With five household vehicle replacement scenarios, we assessed the annual VMT and tailpipe carbon dioxide (CO2) emissions change associated with vehicle replacement. It is found that adopting AEVs can potentially reduce more than 5 megatons of CO2 yearly, which is approximately 30% of the total CO2 emitted by internal combustion engine (ICE) cars in the region.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Admissible Powertrain Alternatives for Heavy-Duty Fleets: A Case Study on Resiliency and Efficiency

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H 2 ), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H 2 and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H 2 , and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H 2 and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping.

BEV↗
DOE OSTI2022

Quantifying System Level Impact of Connected and Automated Vehicles in an Urban Corridor

Numerous studies have demonstrated significant energy reduction for an ego vehicle by up to 20% leveraging Vehicle-to-Everything (V2X) technologies [1-4]. Some studies have also analyzed the impact of such vehicles on the energy consumption of other vehicles in a suburban or a highway corridor [5, 6], but the impact in an urban setting has not been studied yet. Southwest Research Institute (SwRI), in collaboration with Continental and Hyundai, is currently working on a Department of Energy funded project that is focused on quantifying the impact of multiple ego vehicles (smart vehicles) on the total energy consumption of the corridor under various traffic conditions, vehicle electrification level, vehicle-to-vehicle (V2V) technology penetration, and the number of smart (ego) vehicles in an urban setting. A six-kilometer-long urban corridor from Columbus, Ohio was modeled and calibrated with real-world data in PTV Vissim traffic microsimulation software. Five forward-looking powertrain models, consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles, were developed to estimate the energy consumption of vehicles on the corridor. A comprehensive full factorial simulation study was performed. The simulation results indicate that for a traffic mix based on projected new vehicles sales in 2025, a 15% corridor-level energy consumption reduction can be achieved. The paper details the development and validation of the simulation framework, design of experiments conducted, a discussion of challenges faced, and results under various test conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Advancing Transportation Efficiency and Electric Vehicles in Tonga: A Review of Relevant Trends, Best Practices, and Future Work

Tonga is facing a transportation sector characterized by private passenger vehicles, poorly maintained roads and walkways, and an inadequate public transit system. By understanding detailed global and regional trends for transport energy efficiency and electric vehicles (EVs) within this context, the Government of Tonga can proactively plan its future transportation systems. In addition to global and regional trends, this presentation also briefly examines Tonga's own transportation policies and actions. Jurisdictions leading in EV adoption have implemented policies such as reducing taxes on EVs compared to internal combustion engine (ICE) vehicles, providing subsidies and rebates for EV charger installation, instituting an age limit on imported ICE vehicles, and developing EV maintenance courses to expand the skill set of current automotive technicians. This presentation was developed for the Pacific Islands Workshop on Electric Mobility, held from November 28th to November 30th 2022 in Fiji. NREL presented virtually and the conference was sponsored by the Pacific Centre for Renewable Energy and Energy Efficiency (PCREEE).

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2013

Artificial Spin Ice: Controlling Geometry, Engineering Frustration

Frustration – the presence of competing interactions – is ubiquitous in the physical sciences and is a source of degeneracy and disorder, which in turn give rise to new and interesting physical phenomena. Perhaps nowhere does it occur more simply than in correlated spin systems, where it has been studied in the most detail. In disordered magnetic materials, frustration leads to spin-glass phenomena, with analogies to the behavior of structural glasses and neural networks. In structurally ordered magnetic materials, it has also been the topic of extensive theoretical and experimental studies over the past two decades. Such geometrical frustration has opened a window on a wide range of fundamentally new exotic behavior. This includes spin liquids in which the spins continue to fluctuate down to the lowest temperatures; and spin ice, which appears to retain macroscopic entropy even in the low temperature limit where it enters a topological Coulomb phase. In the past seven years a new perspective has opened in the study of frustration through the creation of artificial frustrated magnetic systems. These materials consist of arrays of lithographically fabricated single-domain ferromagnetic nanostructures that behave like giant Ising spins. The nanostructures’ interactions can be controlled through appropriate choices of their geometric properties and arrangement on a (frustrated) lattice. The degrees of freedom of the material can not only be directly tuned, but also individually observed. Experimental studies have unearthed intriguing connections to the out-of-equilibrium physics of disordered systems and non-thermal ‘granular’ materials, while revealing strong analogies to (spin) ice materials and their fractionalized magnetic monopole excitations, lending the enterprise a distinctly interdisciplinary flavor. The experimental results have also been closely coupled to theoretical and computational analyses, facilitated by connections to classic models of frustrated magnetism, whose hitherto unobserved aspects have here found an experimental realization. We review the considerable experimental and theoretical progress in this field, including connections to other frustrated phenomena, and we outline future vistas for progress in this rapidly expanding field.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Determination of Ice Characteristics for Marine Hydroengineering Structures

Problems and potential approaches to determining ice characteristics for sea hydroengineering structures design are considered. A system for numerical modeling of ice dynamics is presented. The system may be used to define ice characteristics on approaches to structures with due regard for local hydrometeorological conditions and ice loads on structures. System application examples are presented for determining computational scenarios for ice loads at structures of the Pevek floating nuclear power plant (FNPP), as well as for the breakwater pier under reconstruction in Vanino. A scenario approach is used to determined ice loads.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Phase transitions in few-monolayer spin ice films

Vertex models are an important class of statistical mechanical system that admit exact solutions and exotic physics. Applications include water ice, ferro- and antiferro-electrics, spin ice and artificial spin ice. Here we show that it is possible to engineer spin ice films with atomic-layer precision down to the monolayer limit. Specific heat measurements show that these films, which have a fundamentally different symmetry to bulk spin ice, realise systems close to the two-dimensional F-model, with exotic phase transitions on topologicallyconstrained configurational manifolds. Our results show how spin ice thin films can release the celebrated Pauling entropy of spin ice without an anomaly in the specific heat. They also significantly expand the class of vertex models available to experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Investigating anode off-gas under spark-ignition combustion for SOFC-ICE hybrid systems

Solid oxide fuel cell – internal combustion engine (SOFC-ICE) hybrid systems are an attractive solution for electricity generation. The system can achieve up to 70% theoretical electric power conversion efficiency through energy cascading enabled by utilizing the anode off-gas from the SOFC as the fuel source for the ICE. Experimental investigations were conducted with a single cylinder Cooperative Fuel Research (CFR) engine by altering fuel-air equivalence ratio (φ), and compression ratio (CR) to study the engine load, combustion characteristics, and emissions levels of dry SOFC anode off-gas consisting of 33.9% H 2 , 15.6% CO, and 50.5% CO 2 . The combustion efficiency of the anode off-gas was directly evaluated by measuring the engine-out CO emissions. The highest net-indicated fuel conversion efficiency of 31.3% occurred at φ = 0.90 and CR = 13:1. These results demonstrate that the anode off-gas can be successfully oxidized using a spark ignition combustion mode. The fuel conversion efficiency of the anode tail gas is expected to further increase in a more modern engine architecture that can achieve increased burn rates in comparison to the CFR engine. NO x emissions from the combustion of anode off-gas were minimal as the cylinder peak temperatures never exceeded 1800 K. This experimental study ultimately demonstrates the viability of an ICE to operate using an anode off-gas, thus creating a complementary role for an ICE to be paired with a SOFC in a hybrid power generation plant.

Engineering↗
DOE OSTI2024

Development of Hydrogen-Fueled, Highly Energy-Efficient, Electro-Hydraulic Excavator

Regulations for CO2 emissions reduction are driving increasing demand for “zero emissions” off-road vehicles (ORVs). Hydrogen internal combustion engines (H2 ICEs) are closer to commercial viability, and less expensive than fuel cells. The major challenge is the slow torque response of H2 ICEs, that makes it difficult to sustain the harsh torque requirements typical of ORVs. The goal of this paper is to overcome this challenge by proposing an electro-hydraulic hybrid technology with hybrid powertrain and multi-pressure rail technology as fluid power system for an excavator, to enable the use of the H2 ICE, while improving efficiency. The study is based on simulation in MATLAB/Simulink environment, using duty cycle data from a 24-ton excavator. Simulation results show that in respect to the diesel-powered baseline, the proposed electro-hydraulic hybrid allows the use of a H2 ICE, eliminates CO2 emissions and ensures a 52% fuel consumption reduction.

08 HYDROGEN↗
DOE OSTI2022

Crystallizing Kagome Artificial Spin Ice

Artificial spin ices are engineered arrays of dipolarly coupled nanobar magnets. They enable direct investigations of fascinating collective phenomena from their diverse microstates. However, experimental access to ground states in the geometrically frustrated systems has proven difficult, limiting studies and applications of novel properties and functionalities from the low energy states. In this work, we introduce a convenient approach to control the competing diploar interactions between the neighboring nanomagnets, allowing us to tailor the vertex degeneracy of the ground states. We achieve this by tuning the length of selected nanobar magnets in the spin ice lattice. We demonstrate the effectiveness of our method by realizing multiple low energy microstates in a kagome artificial spin ice, particularly the hardly accessible long range ordered ground state—the spin crystal state. Our strategy can be directly applied to other artificial spin systems to achieve exotic phases and explore new emergent collective behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks

Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Polarimetric Radar Variables in Snowfall at Ka- and W-Band Frequency Bands: A Comparative Analysis

Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (K DP ) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band Z DR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed Z DR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.

42 ENGINEERING↗
DOE OSTI2018

Antifreeze Hydrogels from Amphiphilic Statistical Copolymers

Prevention of ice formation is a critical issue for many applications, but routes to overcome the large thermodynamic driving force for crystallization of water at significant supercooling are limited. Here, we demonstrate that supramolecular hydrogels formed from statistical copolymers of 2-hydroxyethyl acrylate (HEA) and 2-( N -ethylperfluorooctane sulfonamido)ethyl methacrylate (FOSM) exhibit a degree of ice formation suppression unprecedented in a synthetic material. The mechanisms of ice prevention by these hydrogels mimic two methods used by nature: (1) hydrogen bonding of water to highly hydrophilic macromolecular chains and (2) nanoconfinement of water between hydrophobic moieties. From systematic variation in the copolymer composition to control the nanoscale (<4 nm) separation of the self-assembled hydrophobic nanodomains, the main mechanism by which these supramolecular hydrogels inhibit large amounts of water from freezing appears to be soft nanoconfinement. Nearly complete ice inhibition was achieved in hydrogels when the nanodomain separation was <3 nm (i.e., confinement volume ~15 nm 3 ) where <290 water molecules are present. Dielectric spectroscopy is consistent with two primary populations of water: a population of water with a bulk-like dynamics as well as T g (136 K) and a minority population of water with suppressed dynamics and an enhanced T g near 151 K that is attributed to interfacial water. The nanostructured design of these supramolecular hydrogels provides a blueprint concept for controlling and manipulating ice formation in concentrated soft matter using the length scale between hydrophobic domains and the hydrophilicity of the network water-soluble component. Furthermore, these insights have the potential to provide solutions to challenges with ice in engineering applications where confinement of water to nanoscale dimensions is possible.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multidimensional Modeling of Mixture Formation in a Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection

Hydrogen (H 2 ), as a carbon-free fuel, is considered as one of the most promising solutions to reduce the carbon footprint of hard-to-decarbonize energy and transportation sectors. As such, hydrogen-fueled internal combustion engines (H 2 ICEs) have recently been receiving increasing attention, particularly in applications such as on-road/off-road heavy-duty transport and combined heat and power. The direct injection (DI) of gaseous hydrogen into the combustion chamber offers great potential for achieving high power density and high engine efficiency, while mitigating the risk of backfire and reducing pre-ignition. However, the numerical simulation of H 2 DI system remains a formidable challenge associated with the high computational cost of reproducing compressible supersonic flow and shocks in narrow injector passages and in near-nozzle regions. In general, there is a lack of well-established and validated practices for the modeling of high-pressure H 2 DI in large-bore engines. Here, to this end, this study focuses on computational fluid dynamics (CFD) modeling of the mixture formation process in a heavy-duty optical engine employing a medium-pressure H 2 DI system. Both large eddy simulations (LES) and Reynolds Averaged Navier–Stokes (RANS) simulations are performed and evaluated against optical data. Gaseous hydrogen is injected into the combustion chamber via a centrally located outward opening hollow-cone injector at a pressure of 40 bar. Simulations are carried out for two injection timings, namely, −120 and −60 °CA. The numerical predictions for H 2 distribution in different horizontal and vertical planes during the compression stroke are systematically compared against optical data obtained through planar laser-induced fluorescence (PLIF) measurements. Overall, the LES approach using the Dynamic Structure model is found to have good predictive capabilities for the early jet penetration in terms of length and shape, as well as the later H 2 distributions. However, the unsteady RANS approach with the renormalization group $k - ϵ$ model, which is widely used by industry to model heavy-duty ICEs, significantly underpredicts the H 2 mixing, even at similar mesh resolution to that used in LES. These results indicate that there is a need for the improvement of mixing submodels within the RANS approach when applied to H 2 DI simulations.

LES↗