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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

A Hybrid Transfer Function Procedure for Broadband Auralization Within Small Flight Vehicle Interiors

Auralization of the sound fields within flight vehicle interiors is of interest to aircraft designers considering crew and passenger ride quality. Computer models can be used to predict the filtering effects that the fuselage structure and interior volume have on the exterior sound field in order to simulate the resulting cabin interior noise. However, this can become expensive when a wide frequency range of analysis suitable for passenger auralization is desired. This is particularly the case when using deterministic modeling methods such as finite element analysis (FEA) wherein prohibitively high levels of detail and finite element discretizations are required to resolve the wavelengths at high frequencies. On the contrary, Statistical Energy Analysis (SEA), while not capturing important modal behaviors at low frequencies, is better suited for higher frequency analyses where structural and interior dynamics exhibit high modal overlap and spatially averaged physical quantities suffice. For this reason, a hybrid procedure is considered here that utilizes FE modal solutions at lower frequencies and SEA results at higher frequencies. A transfer function dataset containing the exterior to interior vibroacoustic filtering effect is calculated accordingly and subsequently stored to be used for a variety of exterior dynamic load cases. At low frequencies, the transfer function dataset detail is related to the level of FE model refinement whereas at high frequencies it functions as an equalizer array with a level of detail corresponding to the SEA subsystem partitioning scheme. The above procedure is described herein and demonstrated on a six passenger flight vehicle with eight propulsors in a hover flight condition.

auralization↗
DOE OSTI2024

China's plug-in hybrid electric vehicle transition: An operational carbon perspective

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon–neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China’s top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2–38.2 kWh/100 km) was 30–40 % higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7–23.5 L/100 km) was 3–6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon–neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Array↗
NASA NTRS2012

Technology Investment Agendas to Expand Human Space Futures

The paper develops four alternative core-technology advancement specifications, one for each of the four strategic goal options for government investment in human space flight. Already discussed in the literature, these are: Explore Mars; Settle the Moon; accelerate commercial development of Space Passenger Travel; and enable industrial scale-up of Space Solar Power for Earth. In the case of the Explore Mars goal, the paper starts with the contemporary NASA accounting of ?55 Mars-enabling technologies. The analysis decomposes that technology agenda into technologies applicable only to the Explore Mars goal, versus those applicable more broadly to the other three options. Salient technology needs of all four options are then elaborated to a comparable level of detail. The comparison differentiates how technologies or major developments that may seem the same at the level of budget lines or headlines (e.g., heavy-lift Earth launch) would in fact diverge widely if developed in the service of one or another of the HSF goals. The paper concludes that the explicit choice of human space flight goal matters greatly; an expensive portfolio of challenging technologies would not only enable a particular option, it would foreclose the others. Technologies essential to enable human exploration of Mars cannot prepare interchangeably for alternative futures; they would not allow us to choose later to Settle the Moon, unleash robust growth of Space Passenger Travel industries, or help the transition to a post-petroleum future with Space Solar Power for Earth. The paper concludes that a decades-long decision in the U.S.--whether made consciously or by default--to focus technology investment toward achieving human exploration of Mars someday would effectively preclude the alternative goals in our lifetime.

human space flight↗
NASA NTRSDate not supplied

A Proposed Taxonomy for Advanced Air Mobility

There has been a large growth in interest of utilizing new technologies—most notably electrified propulsion and automation—as well as new business models to bring aviation services into the daily lives of a greater segment of society. Generally, these services are envisioned to augment existing ground modes of transportation or to enable new operating capabilities for shorter-range aviation missions. These services, which have become known as advanced air mobility (AAM), include passenger transportation, cargo transportation, and aerial work missions, such as aerial photography. In this paper we describe advanced air mobility and provide a framework based on demand and supply concepts that can be used for developing a taxonomy for AAM with a focus on passenger applications. This taxonomy is intended to facilitate the nascent AAM stakeholder community in adopting a common terminology and to enable better coordination among disparate AAM research and development activities.

advanced air mobility↗
DOE OSTI2024

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities

Automated Mobility Platform (AMP) is a general term for intelligently managed passenger mobility systems that leverage automation and electrification to improve system performance and sustainability through the integration of real-time data streams with advanced decision-making processes tailored for use in large facilities. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for passengers with reduced mobility [PRMs]) and sustainability - both of which are important metrics often neglected during the engineering design process. Therefore, to evaluate systems and inform critical decisions more effectively, a holistic evaluation framework is proposed that includes equity, sustainability, and infrastructure perspectives - referred to as a multidimensional benefit assessment. More specifically, the following analysis focuses on: (1) mobility, with emphasis on travel time and accessibility within an airport, contrasting existing systems with the proposed AMP system; (2) equity analysis, specifically to the PRM community, but with an eye to the whole of society; (3) energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, comparing and contrasting modes as well as impacts on overall airport operations efficiency; and (4) fundamental changes in building design enabled by AMPs for larger and more flexible, functional, and energy-efficient structures.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

The contribution of bioenergy to the decarbonization of transport: a multi-model assessment

The expected growth in the demand for passenger and freight services exacerbates the challenges of reducing transport GHG emissions, especially as commercial low-carbon alternatives to petroleum fuels are limited for shipping, air and long-distance road travel. Biofuels can offer a pathway to significantly reduce emissions from these sectors, as they can easily substitute for conventional liquid fuels in internal combustion engines. In this paper, we assess the potential of bioenergy to reduce transport GHG emissions through an analysis leveraging various integrated assessment models and scenarios, as part of the 33rd Energy Modeling Forum study (EMF-33). Furthermore, we find that bioenergy can contribute a significant, albeit not dominant, proportion of energy supply to the future transport sector: in scenarios aiming to keep the temperature increase below 2 °C by the end of the twenty-first century, models project that in 2100 bioenergy can provide on average 42 EJ/yr (ranging from 5 to 85 EJ/yr) for transport (compared to 3.7 EJ in 2018), mainly through lignocellulosic fuels. This makes up 9–62% of final transport energy use. Only a small amount of bioenergy is projected to be used in transport through electricity and hydrogen pathways, with a larger role for biofuels in road passenger transport than in freight. The association of carbon capture and storage (CCS) with bioenergy technologies (BECCS) is a key determinant in the role of biofuels in transport, because of the competition for biomass feedstock to provide other final energy carriers along with carbon removal. Among models that consider CCS in the biofuel conversion process the average market share of biofuels is 21% in 2100 (ranging from 2 to 44%), compared to 10% (0–30%) for models that do not. Cumulative direct emissions from the transport sector account for half of the emission budget (from 306 to 776 out of 1,000 GtCO 2 ). However, the carbon intensity of transport decreases as much as other energy sectors in 2100 when accounting for process emissions, including carbon removal from BECCS. Lignocellulosic fuels become more attractive for transport decarbonization if BECCS is not feasible for any energy sectors. Since global transport service demand increases and biomass supply is limited, its allocation to and within the transport sector is uncertain and sensitive to assumptions about political as well as technological and socioeconomic factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Optimizing first- and last-mile public transit services leveraging transportation network companies (TNC)

First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (– 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. Here, the Hybrid service was significantly cheaper to operate (– 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Characterization of recent wild pig-vehicle collisions in Georgia, USA

Vehicle collisions with wild pigs (Sus scrofa) are reported almost everywhere this species is found. However, this is one of the least studied and characterized forms of damage that these invasive animals cause in the United States (U.S.). Here, we analyzed 518 wild pig-vehicle collisions (WPVCs) that took place statewide in Georgia between 2015 and 2021. From that dataset, we analyzed several parameters in order to better understand and characterize these accidents on a scale that had previously not been done in the U.S. Wild pig-vehicle collisions were reported from 105 out of the 159 counties in Georgia, increasing in number annually over the seven-year period. WPVCs were most likely to occur in the fall (37%). A duration weighted time of day analysis showed that WPVCs were most frequent at dusk. Most (97%) reported accidents were caused by live wild pigs, with the remainder being due to collisions with already dead or road-killed pigs. Most (86%) collisions involved a single wild pig while the remainder occurred with two or more pigs. Collisions occurred mostly with passenger cars on dry, straight, and level two-lane blacktop roads under dark, unlighted conditions. Nine percent of the vehicle accidents involving wild pigs resulted in injuries to the drivers and passengers. Some of the most severe injuries reported were caused by swerving to avoid striking wild pigs. No human fatalities were recorded due to these accidents. Wild pig-vehicle collisions are costly and dangerous and should be closely monitored and mitigated by the agencies responsible for motorist safety, transportation infrastructure, and wildlife management.

Georgia↗
DOE OSTI2025

Transportation in net-zero emissions futures: Insights from the EMF-37 model intercomparison study

Transportation is currently the largest source of U.S. anthropogenic CO 2 emissions, at about a third of the total. Achieving net-zero emissions by mid-century will require substantial reductions in transportation emissions across passenger and freight travel. Here we leverage a model intercomparison study to explore the role of transportation in scenarios achieving net-zero economy-wide CO 2 emissions by 2050. We find the transport sector is poised to play the most significant role in reducing demand-side emissions, mostly driven by technology substitution, as modeling results suggest a limited role for mode shifting and for reduced use of personal car travel in the U.S. Among various technology solutions, models show agreement that passenger on-road vehicles will largely transition to electric vehicles (EVs), while solutions to decarbonize heavier travel modes are more diverse and include greater use of liquid biofuels and hydrogen. Research should continue to investigate the evolution of on-road electrification, the role of biofuels and hydrogen across heavier travel modes, and the role of mode shifting and travel behavior change to support personal transportation decarbonization at national and regional scales to temper the rapid growth in clean fuel and electricity demand.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. Here, the framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Trends and 2025 Insights on the Rise of Electric Vehicles in the USA

Plug-in electric vehicles (EVs) are reshaping the transportation energy landscape, providing a practical alternative to petroleum fuels for a growing number of applications. EV sales grew 55x in the past decade (2014-2024) and 6x since 2020, driven by technological progress enabled by policies to reduce transportation emissions as well as industrial plans motivated by strategic value of EVs for global competitiveness, jobs and geopolitics. In 2024, 22% of passenger cars sold globally were EVs and opportunities for EVs beyond on-road applications are growing, including solutions to electrify off-road vehicles, maritime and aviation. This Review updates and expands our 2020 assessment of the scientific literature and describes the current status and future projections of EV markets, charging infrastructures, vehicle-grid integration and supply chains in the USA. EV is the lowest-emission motorized on-road transportation option, with life-cycle emissions decreasing as electricity emissions continue to decrease. Charging infrastructure grew in line with EV adoption but providing ubiquitous reliable and convenient charging remains a challenge. EVs are reducing electricity costs in several US markets and coordinated EV charging can improve grid resilience and reduce electricity costs for all consumers. The current trajectory of technology improvement and industrial investments points to continued acceleration of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

An Integrated Traction Drive with a High Speed Surface Permanent Magnet External Rotor Motor for Electric Vehicles

This work presents the detailed design of an outer rotor motor and integrated drive for passenger electric vehicles. The motor uses non-heavy rare earth permanent magnets and operates at a top speed of 20,000rpm. In-slot ceramic heat exchangers are utilized for the cooling of the windings. The external rotor configuration of the motor leaves a significant amount of space in the stator bore, which is leveraged for the integration of the power electronics. A high speed external rotor motor creates a number of mechanical, thermal and assembly challenges and this work presents detailed thermal, mechanical and rotordynamics analysis.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Silicon Carbide Inverter for Off-Road Heavy-Duty Applications: The Importance of Thermal and Thermomechanical Design in Power Electronics Packaging

Electrification of drivetrain systems is now seen as a major opportunity by the transportation industry across the globe to reduce the greenhouse gas emissions and revolutionize the travel patterns of millions of people. The cumulative number of plug-in hybrid and battery electric vehicles (EVs) sold in the United States has now surpassed 2 million in 2021, according to the International Energy Agency. The introduction of EVs in different classes of passenger vehicles and the continued drop in prices spurred by government incentives have attracted the attention of consumers despite certain barriers, such as higher initial cost and range anxiety. In the United States, the EV market share is now growing at an exponential pace, with the domestic automakers allocating a lion's share of new and future car sales to EVs. Additionally, different market studies project decreasing cost and rising sales of medium- and heavy-duty electric trucks. Although electrification initiatives are strongly pursued in the on-road passenger vehicle market, off-highway sectors, such as construction, mining, and agriculture, are also gradually implementing electric drivetrain technologies in their machineries. As EVs grow in popularity on a global scale, innovative drivetrain technologies must meet the increasing energy demand by significantly increasing system efficiency.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗