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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Towards Automated Design of Corrosion Resistant Alloy Coatings with an Autonomous Scanning Droplet Cell

We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization. Automation and machine learning are currently driving rapid innovation in high-throughput and autonomous materials design and discovery. We present two alloy design vignettes: one focusing on a multi-objective corrosion resistant alloy optimization and a study highlighting the complexity of the multimodal characterization needed to provide insight into the underlying structural and chemical factors that drive observed material behavior. This motivates a close coupling between autonomous research platforms and scientific machine learning methodology that blends mechanistic physical models and black box machine learning models. Lastly, we reflect on our early efforts in on-demand alloy deposition, highlighting some of the challenges. This emerging research area presents new opportunities to accelerate materials synthesis, evaluation, and hence discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2024

Dynamic metal-linker bonds in metal–organic frameworks

Metal-linker bonds serve as the “glue” that binds metal ions to multitopic organic ligands in the porous materials known as metal–organic frameworks (MOFs). Despite ample evidence of bond lability in molecular and polymeric coordination compounds, the metal-linker bonds of MOFs were long assumed to be rigid and static. Given the importance of ligand fields in determining the behaviour of metal species, labile bonding in MOFs would help explain outstanding questions about MOF behaviour, while providing a design tool for controlling dynamic and stimuli-responsive optoelectronic, magnetic, catalytic, and mechanical phenomena. Here, in this work, we present emerging evidence that MOF metal-linker bonds exist in dynamic equilibria between weakly and tightly bond conformations, and that these equilibria respond to guest–host chemistry, drive phase change behavior, and exhibit size-dependence in MOF nanoparticles.

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

Strain-dependent insulating state and Kondo effect in epitaxial SrIrO 3 films

The large spin-orbit coupling in iridium oxides plays a significant role in driving novel physical behaviors, including emergent phenomena in the films and heterostructures of perovskite and Ruddlesden-Popper iridates. Here, in this Letter, we study the role of epitaxial strain on the electronic behavior of thin SrIrO 3 films. We find that compressive epitaxial strain leads to metallic transport behavior, but a slight tensile strain shows gapped behavior. Temperature-dependent resistivity measurements are used to examine different behaviors in films as a function of strain. We find Kondo contributions to the resistivity, with stronger effects in films that are thinner and under less compressive epitaxial strain. These results show the potential to tune SrIrO 3 into Kondo insulating states and open possibilities for a quantum critical point that can be controlled with strain in epitaxial films.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding the electron-water interaction at the molecular level: Integrating theory and experiment in the cluster regime

In this final technical report, we summarize our major accomplishments resulting from a combined experimental-theoretical approach that exploits the unique properties of molecular clusters to elucidate how local interactions at the molecular level drive the macroscopic behavior of materials and chemical transformations in liquids. The specific targets for our efforts have need to elucidate how charged entities are accommodated by hydrogen-bonding networks and to establish how the behavior of room temperature ionic liquids can be tailored for specific chemical applications. Because of their central importance in aqueous electrolyte chemistry, we have placed particular emphasis understanding the accommodation of excess electrons and protons by water. These surprisingly complex fundamental electrical charges play a fundamental role in a wide range of chemical and biological processes, and yet even a qualitative picture of their speciation in water and at the air-water interface has proven elusive. The main experimental diagnostic in our approach is to measure and analyze vibrational spectra of temperature and composition controlled molecular aggregates that isolate and amplify the spectral markers for local interactions that are often obscured in measurement on bulk or interfacial water. Our combined experimental-theoretical program has led to a greatly enhanced understanding of the nature of excess electrons and protons in water and in the process developed new and powerful methods of chemical analysis that are currently in use across the chemical sciences.

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

Western Smart Regional EV Adoption and Infrastructure at Scale- WestSmartEV@Scale (Budget Period 2 – Milestone Report – TNC EV Study)

WestSmartEV@Scale is a comprehensive research project addressing key opportunities to decarbonize transportation throughout the Rocky Mountain West. Building upon the framework established in the original WestSmart project, unique subprojects are scoped targeting transportation emissions across light-, medium-, and heavy-duty vehicles with consideration of equity and access. This report summarizes the progress to date on Subproject 6, TNC EV Study (Urban Mobility). It is comprised of three sections: • Outreach and participant recruitment; • Survey data collection and analysis; and • BEV driver driving and charging behavior data collection and analysis.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Detection of hazardous driving using machine learning

An autonomous driving system could create or exacerbate a hazardous driving situation due to incorrect machine learning, algorithm design, sensor limitations, environmental conditions or other factors. This technology presents solutions that use machine learning to detect when the autonomous driving system is in this state e.g., erratic or reckless driving and other behavior, in order to take remedial action to prevent a hazard such as a collision.

Shirvani, Philip↗
DOE OSTI2021

Clogging, dynamics, and reentrant fluid for active matter on periodic substrates

Here, we examine the collective states of run-and-tumble active matter disks driven over a periodic obstacle array. When the drive is applied along a symmetry direction of the array, we find a clog-free uniform liquid state for low activity, while at higher activity, the density becomes increasingly heterogeneous and an active clogged state emerges in which the mobility is strongly reduced. For driving along nonsymmetry or incommensurate directions, there are two different clogging behaviors consisting of a drive-dependent clogged state in the low activity thermal limit and a drive-independent clogged state at high activity. These regimes are separated by a uniform flowing liquid at intermediate activity. There is a critical activity level above which the thermal clogged state does not occur, as well as an optimal activity level that maximizes the disk mobility. Thermal clogged states are dependent on the driving direction while active clogged states are not. In the low activity regime, diluting the obstacles produces a monotonic increase in the mobility; however, for large activities, the mobility is more robust against obstacle dilution. We also examine the velocity-force curves for driving along nonsymmetry directions and find that they are linear when the activity is low or intermediate but become nonlinear at high activity and show behavior similar to that found for the plastic depinning of solids. At higher drives, the active clustering is lost. For low activity, we also find a reentrant fluid phase, where the system transitions from a high mobility fluid at low drives to a clogged state at higher drives and then back into another fluid phase at very high drives. We map the regions in which the thermally clogged, partially clogged, active uniform fluid, clustered fluid, active clogged, and directionally locked states occur as a function of disk density, drift force, and activity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. Although the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “intelligent driver model” (IDM) as our driver behavior model and use real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations, and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Dynamically Collected Local Density using Low-Cost Lidar and its Application to Traffic Models

This article demonstrates the use of traffic density observations collected dynamically in the vicinity of probe vehicles. Fixed position sensors cannot capture the longitudinal evolution of local traffic density in the corridor. In this research, dynamic traffic density observations were collected in a naturalistic driving setting that was free of any controlled experiment biases. Speed from global positioning system and space headway from a light detection and ranging module was collected on one arterial and one freeway segment, 2 and 4mi long, respectively. The combined data frequency was approximately 3Hz. Space headway was used to estimate the local density and consequently to identify the density of a specific location in a corridor. Besides, driver behavior was characterized using the relationship between instantaneous speed and local density under different regimes of the Wiedemann car-following model. Macroscopic traffic stream models were used to investigate the relationship between dynamically collected instantaneous speed and local density. Using the longitudinal evolution of density, precise local density across the corridor can be obtained along with the leader and follower trajectories. A method to identify driver behavior across density ranges was developed for different facility types using a microscopic relationship between instantaneous speed and local density. Overall driving behavior on the freeway segment can be represented by translating the instantaneous speed and local density relationship to macroscopic stream models.

Engineering↗
DOE OSTI2026

Understanding Electric Vehicle Range and Charging Needs: Interactions Between Ambient Temperature, Commute Patterns, and State-of-Charge Usage

Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2020

Directional locking effects for active matter particles coupled to a periodic substate

Directional locking occurs when a particle moving over a periodic substrate becomes constrained to travel along certain substrate symmetry directions. Such locking effects arise for colloids and superconducting vortices moving over ordered substrates when the direction of the external drive is varied. Here we study the directional locking of run-and-tumble active matter particles interacting with a periodic array of obstacles. In the absence of an external biasing force, we find that the active particle motion locks to various symmetry directions of the substrate when the run time between tumbles is large. The number of possible locking directions depends on the array density and on the relative sizes of the particles and the obstacles. For a square array of large obstacles, the active particle only locks to the x, y, and 45° directions, while for smaller obstacles, the number of locking angles increases. Each locking angle satisfies θ = arctan (p/q) , where p and q are integers, and the angle of motion can be measured using the ratio of the velocities or the velocity distributions in the x and y directions. When a biasing driving force is applied, the directional locking behavior is affected by the ratio of the self-propulsion force to the biasing force. For large biasing, the behavior resembles that found for directional locking in passive systems. For large obstacles under biased driving, a trapping behavior occurs that is nonmonotonic as a function of increasing run length or increasing self-propulsion force, and the trapping diminishes when the run length is sufficiently large.

36 MATERIALS SCIENCE↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Uncertainty Quantification of Fatigue Behavior of Rough AM Surfaces and Microstructures to Enable Hydrogen Gas Turbine Combustion

Modification of fossil-fueled industrial gas turbines to accept no/low carbon fuels (Hydrogen, H2/natural gas blends) is a significant undertaking. Successful deployment of this technology sits at the intersection of three design criteria (1) new functional fuel injectors that can burn these fuels, (2) manufacturability to meet cost and time-to-market targets, and (3) durability in the harsh environment of an operating turbine. Additive manufacturing (AM) provides accelerated product development. However, uncertainty remains around the durability of parts with rough AM surfaces. A fully experimental approach towards quantifying fatigue performance of rough AM microstructures is costly and laborious. Instead, Solar Turbines Incorporated (Solar) proposes the use of a crystal plasticity finite element (CPFE) model to quantify the factors that drive AM surface fatigue behavior. Solar will use the CPFE results, along with targeted experimental data, to train a computationally efficient surrogate model that can be incorporated into existing turbine part lifing methods.

08 HYDROGEN↗
DOE OSTI2021

Brain Connectivity Workshop Series Report

The National Institutes of Health (NIH) BRAIN Initiative aims to accelerate development and application of technologies that show how individual brain cells in complex neural circuits interact at the speed of thought and action. Complete wiring diagrams of the mammalian brain will revolutionize the capabilities of researchers to formulate and test models of how activity within brain circuits drives coordinated function and behavior. A top priority for the BRAIN Initiative, motivated by the strategic guidance of the BRAIN 2.0 Working Group report, is to muster the resources and foster the collaborations needed to generate these wiring diagrams at the level of long-range projections (“projectomes”) and synapses (“connectomes”) in whole mammalian brains, including those from rodents, humans, and other large-brained mammals. Achieving the scale and resolution necessary to characterize and use these projectomes and connectomes will not only push the boundaries of current imaging and computational capabilities to generate faster, cheaper, and more scalable technologies, but also drive broad innovation in data science, artificial intelligence, and machine learning.

42 ENGINEERING↗
DOE OSTI2022

Understanding thermal and mechanical effects on lithium plating in lithium-ion batteries

Lithium plating is one of the main concerns for cell durability, as cyclable lithium and cell capacity are decreased during the process. Thermal and mechanical loads can influence cell behavior, including lithium plating driving forces, as suggested by the Arrhenius equation and the Bruggeman relation, respectively. Hence, a 1D and a 3D model are employed to study the thermal and mechanical effects on lithium plating in a lithium-ion battery with a high areal capacity (4 mAh/cm 2 ). The 1D model assumes uniform mechanical and temperature profile, while the 3D model is capable of capturing the nonuniformity of stress and temperature in the cell. Further, the effects of mechanical deformation and different temperatures on the process of lithium plating are investigated in this study. With the help of the 1D charging simulations, it is observed that lower temperatures increase the risk of lithium plating and the presence of mechanical deformations can accelerate its initiation. Furthermore, 3D charging simulations indicated that folds and boundaries of jellyrolls are more susceptible to lithium plating due to the higher stress concentration and lower temperature, respectively.

25 ENERGY STORAGE↗
DOE OSTI2023

Effect of dielectric target properties on plasma surface ionization wave propagation

Surface ionization waves (SIWs) propagating along dielectric covered, grounded surfaces have been studied for various dielectric bulk and surface conditions; a dependence on the propagation velocity with respect to dielectric electrical thickness and near surface permittivity profiles are observed. SIWs generated by an atmospheric pressure plasma source are imaged interacting with planar dielectric surface. Surface wave velocity is obtained by tracking emission intensity as a function of time. Target dielectric thickness is varied from $d = 0.15-10$ mm and dielectric constant is varied from $\epsilon_r = 6.21 - 9.4$. The propagation of SIWs can be generally predicted by relating their velocity to the RC time constant of the circuit generated between the plasma and the dielectric surface, but it is found that this approximation breaks down for dielectric substrates of sufficient thickness and wave velocity becomes constant. The results show that wave velocity is stable and predictable for target thicknesses beyond a certain point determined by the permittivity of the target material. It is also shown that SIW propagation is strongly driven by the dielectric material near to the surface of the target in addition to the bulk material. The possible mechanisms driving these thickness dependent behaviors is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗