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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach

The broader ambition of this article is to popularize an approach for the fair distribution of the quantity of a system's output to its subsystems while allowing for underlying complex subsystem level interactions. Particularly, we present the use of this framework on a very specific (but generalizable) application, interesting for a more general AI audience. Here, we detail a data-driven approach to vehicle price modeling and its component price estimation by leveraging a combination of concepts from machine learning and game theory. We show an alternative to common teardown methodologies and surveying approaches for component and vehicle price estimation at the manufacturer's suggested retail price (MSRP) level that has the advantage of bypassing uncertainties involved in gathering teardown data, the need to perform expensive and biased surveying, and the need to perform retail price equivalent or indirect cost multiplier adjustments to mark up direct manufacturing costs to MSRP. This novel exercise not only provides accurate pricing of the technologies at the customer level, but also shows the, a priori known, large gaps in pricing strategies between manufacturers, vehicle classes, market segments, etc. There is also clear interaction between the price of technologies and other specifications present in vehicles. Those results are indication that old methods of manufacturer-level component costing, aggregation, and application of flat and rigid adjustment factors should be carefully examined. The findings are based on a database developed by Argonne, which includes over 64,000 vehicles covering MY1990 to MY2020 with hundreds of vehicle specs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Trends and Heterogeneity in Electric Vehicle Economics: A French Case Study

The total cost of ownership of electric vehicles depends on both the behavior and economic context of the driver as well as the level of government support offered for electrified vehicles. In this work, we study both of these effects as they shift private vehicle costs and emissions benefits, focused on a case study of the faculty and staff commuters to the Cite Scientifique campus of the University of Lille. The modeling approach considers the net costs and emissions from switching each driver from an internal combustion engine vehicle to three electrified options: a hybrid electric, a plug-in hybrid, and a pure battery electric. Increasing government support or technology-induced cost declines of electric vehicles both improve the economics of electrified options and shift more drivers towards battery electrics as the preferred alternative to thermal vehicles.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2026

Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only

Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.

Connected and Automated vehicles↗
DOE OSTI2022

Breakdown of Electric Vehicle Supply Equipment Installation Costs

The plug-in electric vehicles (PEVs) market is receiving help from the current political climate, incentives at the federal and state levels, excessive cost of petroleum fuel, growing focus on climate solutions, increasing investment and direction by automobile manufacturers and increased awareness through media reports and advertising. Increasingly, the transportation industry, in both the United States and many other countries, is aimed at electric motive energy where practical. Increased investment in research and development have led to increasing vehicle range and lower battery costs; both of which have been deterrents in the past. The increasing demand for PEVs (consisting of the battery electric vehicle [BEV] and plug-in hybrid electric vehicle [PHEV], is challenged by the need for charging infrastructure to support these vehicles. The BEV relies totally on the on-board battery to supply the motive energy while the PHEV utilizes its battery and an installed internal combustion engine (ICE). The maximum benefit is achieved by using the battery power as much as possible. This arrangement requires the use of battery charging equipment, known as electric vehicle supply equipment (EVSE).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Data Quality Assessment of Optiwatt Vehicle Telematics Data

In October 2024, the Idaho National Laboratory (INL) received data from Optiwatt (Compass Global, Inc.) describing the driving and charging behavior of electric vehicle (EV) drivers. The data shared had been collected from approximately 10,000 vehicles and included vehicle specifications, driving information like odometer readings at the beginning and end of origin-destination pairs (i.e., trips with identification of home for trip start and end for Tesla vehicles), and charging information such as charging energy consumed per charge session and if the charge occurred at home. The vehicle data were provided from 9 EV makes and 18 EV models, with production years ranging from 2012–2024, but more than 9,500 of the vehicles were Tesla EVs. The data includes more than six million trips and more than three million charging events that occurred between June 2023 to Aug 2024 and collected from California and the Eastern United States. The purpose of this report is to review the quality of the data received from Optiwatt and the feedback INL received from Optiwatt after data concerns were shared with them.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Decision-Based Fusion for Vehicle Matching

In this work, a framework is proposed for decision fusion utilizing features extracted from vehicle images and their detected wheels. Siamese networks are exploited to extract key signatures from pairs of vehicle images. Our approach then examines the extent of reliance between signatures generated from vehicle images to robustly integrate different similarity scores and provide a more informed decision for vehicle matching. To that end, a dataset was collected that contains hundreds of thousands of side-view vehicle images under different illumination conditions and elevation angles. Experiments show that our approach could achieve better matching accuracy by taking into account the decisions made by a whole-vehicle or wheels-only matching network.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

A Review and Outlook on Energy Consumption Estimation Models for Electric Vehicles

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. Here, we summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1962

Some Applications of Detailed Wind Profile Data to Launch Vehicle Response Problems

The response of a launch vehicle to a number of detailed wind profiles has been determined. The wind profiles were measured by two techniques which are briefly described. One of these techniques uses an angle-of-attack sensor in conjunction with guidance data to measure the wind profile traversed by some particular launch vehicle. The other wind-measuring technique is a photographic triangulation method, whereby two cameras take simultaneous pictures of a vertical trail of smoke left by a launch vehicle or sounding rocket. The response of a vehicle flying these detailed profiles is compared with the response of the same vehicle flying through balloon-measured profiles. The response to the detailed wind profiles, relative t o the balloon-measured profiles, is characterized by the large excitation of the rigid pitch and elastic bending modes. This is found to cause higher loads on the launch vehicle structure. Established design criteria which utilize balloon measured wind profiles have arbitrarily accounted-for this increased load by adding a load due to some type of discrete gust.

LAUNCH VEHICLE↗
NASA NTRS1962

Development of the Saturn Space Vehicle

The Saturn launch vehicle program has become a major element in our national space effort within little more than 3 years. At the beginning of the program, in 1958, various Saturn configurations were planned to lift instrumented payloads of 20,000 to 40,000 lb. into Earth orbit. As research and development progressed, it became apparent that the vehicle design approach had far more potential than anticipated that it was, in fact, a breakthrough into a new class of heavy launch vehicles. Thus, within the space of a few years, it was possible to consider even more advanced missions. In addition to placing multiton payloads in orbit, we can seriously conceive sending over 90,000 lb. of instruments on escape missions, using advanced Saturn-class vehicles. With these vehicles, we plan to accomplish lunar reconnaissance and manned lunar landings scheduled for the latter part of this decade. Likewise, these vehicles will permit flight tests of advanced nuclear power plants and stages.

SATURN LAUNCH VEHICLE↗
NASA NTRS2014

Aeroacoustics of Space Vehicles

While for airplanes the subject of aeroacoustics is associated with community noise, for space vehicles it is associated with vibro-acoustics and structural dynamics. Surface pressure fluctuations encountered during launch and travel through lower part of the atmosphere create intense vibro-acoustics environment for the payload, electronics, navigational equipment, and a large number of subsystems. All of these components have to be designed and tested for flight-certification. This presentation will cover all three major sources encountered in manned and unmanned space vehicles: launch acoustics, ascent acoustics and abort acoustics. Launch pads employ elaborate acoustic suppression systems to mitigate the ignition pressure waves and rocket plume generated noise during the early part of the liftoff. Recently we have used large microphone arrays to identify the noise sources during liftoff and found that the standard model by Eldred and Jones (NASA SP-8072) to be grossly inadequate. As the vehicle speeds up and reaches transonic speed in relatively denser part of the atmosphere, various shock waves and flow separation events create unsteady pressure fluctuations that can lead to high vibration environment, and occasional coupling with the structural modes, which may lead to buffet. Examples of wind tunnel tests and computational simulations to optimize the outer mold line to quantify and reduce the surface pressure fluctuations will be presented. Finally, a manned space vehicle needs to be designed for crew safety during malfunctioning of the primary rocket vehicle. This brings the subject of acoustic environment during abort. For NASAs Multi-Purpose Crew Vehicle (MPCV), abort will be performed by lighting rocket motors atop the crew module. The severe aeroacoustics environments during various abort scenarios were measured for the first time by using hot helium to simulate rocket plumes in the Ames unitary plan wind tunnels. Various considerations used for the helium simulation and the final confirmation from a flight test will be presented.

launch vehicle↗
NASA NTRS2014

Conceptual Launch Vehicle and Spacecraft Design for Risk Assessment

One of the most challenging aspects of developing human space launch and exploration systems is minimizing and mitigating the many potential risk factors to ensure the safest possible design while also meeting the required cost, weight, and performance criteria. In order to accomplish this, effective risk analyses and trade studies are needed to identify key risk drivers, dependencies, and sensitivities as the design evolves. The Engineering Risk Assessment (ERA) team at NASA Ames Research Center (ARC) develops advanced risk analysis approaches, models, and tools to provide such meaningful risk and reliability data throughout vehicle development. The goal of the project presented in this memorandum is to design a generic launch 7 vehicle and spacecraft architecture that can be used to develop and demonstrate these new risk analysis techniques without relying on other proprietary or sensitive vehicle designs. To accomplish this, initial spacecraft and launch vehicle (LV) designs were established using historical sizing relationships for a mission delivering four crewmembers and equipment to the International Space Station (ISS). Mass-estimating relationships (MERs) were used to size the crew capsule and launch vehicle, and a combination of optimization techniques and iterative design processes were employed to determine a possible two-stage-to-orbit (TSTO) launch trajectory into a 350-kilometer orbit. Primary subsystems were also designed for the crewed capsule architecture, based on a 24-hour on-orbit mission with a 7-day contingency. Safety analysis was also performed to identify major risks to crew survivability and assess the system's overall reliability. These procedures and analyses validate that the architecture's basic design and performance are reasonable to be used for risk trade studies. While the vehicle designs presented are not intended to represent a viable architecture, they will provide a valuable initial platform for developing and demonstrating innovative risk assessment capabilities.

Launch Vehicle↗
NASA NTRS2015

Airborne Simulation of Launch Vehicle Dynamics

In this paper we present a technique for approximating the short-period dynamics of an exploration-class launch vehicle during flight test with a high-performance surrogate aircraft in relatively benign endoatmospheric flight conditions. The surrogate vehicle relies upon a nonlinear dynamic inversion scheme with proportional-integral feedback to drive a subset of the aircraft states into coincidence with the states of a time-varying reference model that simulates the unstable rigid body dynamics, servodynamics, and parasitic elastic and sloshing dynamics of the launch vehicle. The surrogate aircraft flies a constant pitch rate trajectory to approximate the boost phase gravity turn ascent, and the aircraft's closed-loop bandwidth is sufficient to simulate the launch vehicle's fundamental lateral bending and sloshing modes by exciting the rigid body dynamics of the aircraft. A novel control allocation scheme is employed to utilize the aircraft's relatively fast control effectors in inducing various failure modes for the purposes of evaluating control system performance. Sufficient dynamic similarity is achieved such that the control system under evaluation is configured for the full-scale vehicle with no changes to its parameters, and pilot-control system interaction studies can be performed to characterize the effects of guidance takeover during boost. High-fidelity simulation and flight-test results are presented that demonstrate the efficacy of the design in simulating the Space Launch System (SLS) launch vehicle dynamics using the National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center Fullscale Advanced Systems Testbed (FAST), a modified F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois), over a range of scenarios designed to stress the SLS's Adaptive Augmenting Control (AAC) algorithm.

airborne simulation↗
NASA NTRS2024

Concept, Design, & Implementation of a Remote Vehicle Operations Center for Autonomous Missions

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.

CERTAIN↗
NASA NTRSDate not supplied

Effect of Nuclear Thermal Propulsion Engine Thrust and Isp Tradeoffs and Alternate Propellants on ΔV Budget and Architecture Mass for Crewed Mars Vehicles

This paper will provide an overview of a spacecraft architecture model developed at AMA as well as an insight into some of the results provided. Currently, preliminary analysis has been completed with this model on evaluating different thrust to weight ratios of Nuclear Thermal Propulsion based vehicles using hydrogen as the propellant by altering the number of engines in the engine block. Gravity losses are incorporated into this analysis as different thrust to weight ratios will provide different burn times. So far, this analysis has shown that in terms of the vehicle mass, it is best to trade the gravity losses for lower vehicle mass as the engines considered add significant dry mass to the vehicle. The final version of this paper will compare gravity losses and vehicle masses for vehicles using different engine masses as well as the ammonia propellant.

Master equipment list↗
NASA NTRSDate not supplied

Design of a Six-Tiltrotor Concept Vehicle for Urban Air Mobility

NASA has developed a series of aircraft concepts known as reference vehicles for urban air mobility operations to facilitate research and development, enabling system-level comparisons between design candidates. In this study, new six-tiltrotor reference vehicles with full-electric and turboelectric propulsion systems were developed using the NASA Design and Analysis of Rotorcraft (NDARC) tool. The sizing results and performance metrics of these new vehicles are presented, along with comparisons to existing reference vehicles. The turboelectric six-tiltrotor is shown to be the fastest among the turboelectric reference vehicles. Although the full-electric six-tiltrotor is not as fast as the turboelectric version, it still offers considerable speed along with increased safety compared to the tiltrotor reference vehicle.

Six-Tiltrotor↗
DOE OSTI2021

Electric-Drive Vehicles

Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. These vehicles can be divided into three categories: All-electric vehicles and Plug-in hybrid electric vehicles (PHEVs). Together, PHEVs and EVs can also be referred to as electric vehicles (EVs).

hybrid, HEV, plug-in, PHEV, electric vehicle, EV, ↗