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

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Low-Rate Cycling Regeneration Sustains NMC811–Graphite Battery Performance through Extreme Ultrafast Charging

Urban air mobility (UAM) systems, such as electric vertical takeoff and landing (eVTOL) aircraft, require batteries capable of extreme ultrafast charging to support high-frequency operations. Here, in this work, we evaluate the electrochemical stability of NMC811–graphite full cells using a complex mixed-modal protocol designed to simulate the aggressive turnaround demands of UAM missions. We demonstrate that incorporating a periodic low-rate (0.3C) “regeneration” cycle enables cells to maintain 96% capacity retention over 900 cycles, despite repeated exposure to ultrafast pulses. To validate the efficacy of this regeneration mechanism, we performed simplified control experiments comparing continuous 10C charging against a 10C–0.3C interleaved protocol. Advanced characterization reveals that while mechanical particle fracture is pervasive at these rates, the regeneration cycles decelerate the electrochemical degradation by resolving spatially heterogeneous Ni oxidation states and relaxing lithium-ion concentration gradients. These findings provide a vital mechanistic framework for battery operation, proving that restorative charging can effectively delay and mitigate failure in demanding UAM applications.

batteries↗
DOE OSTI2023

Carbon Nanotube and Graphene Oxide Reinforced Composites for Lightning Strike Protection of Composite Structures

Carbon fiber reinforced plastics (CFRP) composites have become the primary materials in the aerospace industry. Future transportation means, such as urban air mobility (UAM), will be employing CFRP structures. However, lightning strike damage on composite structures is a serious concern. CFRP composites possess low through-thickness electrical conductivity (≈ 1 S/m), which can cause extreme resistive heat in the event of lightning strikes. Research on polymeric lightning strike protection has gained momentum in the past few years. Researchers have studied various conductive fillers to improve the CFRP performance against lightning strikes. Carbon nanotubes (CNTs) and graphene oxide (GO) have been studied as future lightning Strike Protection (LSP) materials the most. The use of CNT and GO-based composites has shown improved mechanical properties and improved electrical conductivity of the composites. Researchers investigated CNTs and GO as the fillers to the matrix system, sizing the carbon fibers and the surface coating on top of the CFRP surface. This chapter highlights the remarkable improvement in the lightning damage suppression of CNT and GO-based composites and their structural applications. Special attention is given to their potential use as LSP material in future aircraft.

Kumar, Vipin↗
DOE OSTI2023

Perception Testing in Fog for Autonomous Flight

As the path towards Urban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). Here, this paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.

aeronautics↗
DOE OSTI2020

Urban Traveler - Changes and Impacts: Mobility Energy Productivity (MEP) Metric

For nearly a century, the automobile has been the primary mode of personal transportation in American life. This remains true today as millions of people rely heavily on cars to connect suburbs with cities or to travel long distances—often out of routine or convenience. However, advances in technology are fueling an era of transportation transformation, with the potential to transform a system that has remained virtually unchanged for decades. Aspiring smart cities are wrestling with questions such as: How does mobility impact a person’s quality of life? Would people make different travel choices if they were presented with better information about their mobility options? The ability to quantify the mobility potential of a given location is the first step toward answering these questions. In response, an interdisciplinary team at the National Renewable Energy Laboratory (NREL) has developed the Mobility-Energy Productivity (MEP) metric. The MEP metric provides an avenue to not only measure the mobility potential at a specific location in its current configuration, but also to test how various technological advances (e.g., connected and automated vehicles, plug-in electric vehicles, shared mobility) and infrastructure investments (e.g., building an additional highway lane, constructing a new shopping mall, implementing a transit-oriented development) impact the mobility of that location over time. This presentation details the FY19 progress on the MEP metric project funded through DOE VTO's SMART Mobility Consortium.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Measuring Mobility Potential: A Novel Metric to Quantify Mobility Energy Productivity (MEP) of Transportation Systems

For nearly a century, the automobile has been the primary mode of personal transportation in American life. This remains true today as millions of people rely heavily on cars to connect suburbs with cities or to travel long distances - often out of routine or convenience. However, advances in technology are fueling an era of transportation transformation, with the potential to transform a system that has remained virtually unchanged for decades. The challenges of interconnecting our cities and creating a cross-continental transportation system for military purposes spawned the interstate highway system, generating the age of the automobile. In this century, congestion and mobility challenges of rising urban populations are spawning ever-evolving mobility and communications technologies to connect people to goods, services, and employment within a metropolitan and national context - all of which define a high quality of life. Aspiring smart cities are wrestling with questions such as: How does mobility impact a person's quality of life? Would people make different travel choices if they were presented with better information about their mobility options? Would businesses make different location decisions if they could assess the quality of mobility in that area? The ability to quantify the quality of mobility at a given location is the first step toward answering these questions. In response, an interdisciplinary team at the National Renewable Energy Laboratory (NREL) has developed the Mobility-Energy Productivity (MEP) metric. The MEP metric provides an avenue to not only measure the quality of mobility at a specific location in its current configuration, but also to test how various technological advances (e.g., connected and automated vehicles, plug-in electric vehicles, shared mobility) and infrastructure investments (e.g., building an additional highway lane, constructing a new shopping mall, implementing a transit-oriented development) impact the mobility of that location over time.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Going mobile to address emerging climate equity needs in the heterogeneous urban environment

We report the Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Design and Performance of lithium-Ion Batteries for Achieving Electric Vehicle Takeoff, Flight, and Landing

Today, the burgeoning drive towards global urbanization with over half the earth’s population living in cities, has created major challenges with regards to intracity and intercity transit and mobility. This problem is compounded due to the fact that almost always urbanization and increase in standard of living drives individual automobile ownerships. Over 95% of automobiles are presently powered by some form of fossil fuel and as an unintended consequence, urban centers have also been centers for peak greenhouse gas emissions, a major contributor to global climate change. A revolutionary solution to this conundrum is flight capable electric automobiles or electric aerial vehicles that can tackle both urban mobility and climate change challenges. For such advanced electric platforms, energy storage and delivery component is the vital component towards achieving takeoff, flight, cruise, and landing. The requirements and duty cycle demands on the energy storage system is drastically different when compared to the performance metrics required for terrestrial electric vehicles. As the widely deployed lithium ion-based battery systems are often the primary go-to energy storage choice in electric vehicle related applications, it is imperative that performance metrics and specifications for such batteries towards areal electric vehicles need to be established. In this nascent field, there exists ample opportunities for battery material innovations, understanding degradation mechanism, battery design, development and deployment of battery control and management systems. Thus, this chapter comprehensively discusses battery requirements and identifies battery material chemistries suitable for handling aerial electric automobile duty cycles. The chapter also discusses the battery cell-level metrics pertaining to electrochemical, chemical, mechanical, and structural parameters. Furthermore, specific models for battery degradation, state of health (SOH), capacity and models for full cell performance and degradation are also discussed here. Finally, the chapter also discusses battery safety and future directions of batteries that would power these next generation urban electric aircrafts.

Amin, Ruhul↗
DOE OSTI2024

Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint

The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2025

Travel Patterns and Characteristics of Millennial Population in New York State

This study investigates the travel behaviors, demographics, and transportation preferences of millennials (born 1981–1996), a generation that significantly influences urban living and mobility trends. Using data from the National Household Travel Survey (2009, 2017, and 2022) and other sources, this study examines key factors such as trip rates, trip length, travel modes, trip purposes, and travel time while also analyzing the effects of transportation technologies and COVID-19 on millennials’ travel behaviors in New York State (NYS). Additionally, travel patterns of millennials are compared with those of younger (i.e., Gen Z) and older (i.e., Gen X and baby boomer) generations across different geographical regions in NYS (e.g., New York City).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2021

Ensuring reliable connectivity to cellular-connected UAVs with up-tilted antennas and interference coordination

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.

3GPP↗
DOE OSTI2024

Observations of airflow around a supertall curved building and its impact on temperature and humidity in Houston's urban center

Characterization of realistically shaped skyscrapers embedded in non-uniform neighbourhoods experiencing intricate weather patterns remains inadequately investigated. Aiming to close this gap, the Center for Multiscale Applied Sensing team deployed its mobile observatory in the street canyons around the curved Wells Fargo Plaza skyscraper in downtown Houston, TX. Three deployments allowed airflow observations under different inflow wind and thermodynamic stability conditions. Doppler lidar measurements reveal that when inflow hits the curved wall of the skyscraper, perpendicular canyons experience similar vortex configurations creating two windward and two leeward circulations. Windsond measurements support that buoyancy within the deep street canyons can generate thermal updrafts as strong as 2 m s -1 which is sufficient to overturn the mechanical downwash under gentle wind conditions. Canyons experiencing venting during the daytime were observed to be more thermodynamically stable at night while thermodynamically stable canyons during the day were observed to be more thermodynamically unstable at night owing to the accumulation of heat near street-level. Fourier decomposition of the vertical velocity measurements shows that in all cases flow exhibited high Reynolds numbers and was composed of turbulent eddies of predominantly 6 and 15 min periods. Here, this observational dataset provides insights to assess wind load, pedestrian comfort, urban air mobility, and natural ventilation and may be used as a benchmark for numerical model and wind tunnel studies attempting to represent realistically complex urban conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Interface chemistry informs about cathode and anode degradation during fast charging

Here, the aim of this study is to examine the development and makeup of the Solid Electrolyte Interphase (SEI) and Cathode Electrolyte Interphase (CEI) in lithium-ion batteries during rapid charging. Using X-ray photoelectron spectroscopy (XPS) and depth profiling, we investigate the chemical modifications occurring on the electrode surfaces during the initial formation and subsequent fast charge-discharge cycles. Our research shows that the anode's SEI initially comprises a thin layer rich in ketones, which then transforms into a thicker layer dominated by carbonaceous compounds during rapid charging cycles. Following assembly, lithium fluoride (LiF) quickly becomes a key element of the SEI, and its presence continues to grow substantially during the formation cycle, remaining the primary component throughout subsequent cycles. Initially, the cathode forms a thin oxide layer rich in ketones, with no noticeable carbonaceous CEI. The CEI primarily comprises LiF, which experiences an increase during the formation cycle and retains a thin layer of carbon coating after the initial rapid discharge. These findings show how the interphase layers impact the performance and stability of lithium-ion batteries, especially during fast charging for electric vertical take-off and landing (eVTOL) vehicles.

Fast charging↗