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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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Report characterizes state-of-the-art electric and hybrid (combined electric and heat engine) vehicles. Performance data for representative number of these vehicles were obtained from track and dynamometer tests. User experience information was obtained from fleet operators and individual owners of electric vehicles. Data on performance and physical characteristics of large number of vehicles were obtained from manufacturers and available literature.
Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.
Dynamic test analysis on modal damping forces of Saturn 5 Apollo vehicle
The Vehicle Space Transfer and Recovery (VSTAR) system is designed as a manned orbital transfer vehicle (MOTV) with the primary mission of Satellite Launch and Repair (SLR). Reference materials, calculations and trade studies used in the analysis and selection of VSTAR components. Each major VSTAR system is examined separately. Simple graphs and tables are used to make qualitative comparisons of various VSTAR component candidates. Equations and/or calculations used for a particular analysis are also included where applicable.
A data link system was designed to support flight tests in the NASA Transport Systems Research Vehicle B-737 airplane. The purpose of the flight tests was to evaluate pilot acceptance of using data link as the primary source of communications for strategic and tactical air traffic control clearances, weather information, and company messages. The airborne functional operations of the data link system flight tested in 1990 are described.
As Automated Vehicle (AV) services proliferate, data sharing between AV operators and municipal agents is assuming greater importance. Information on the dynamic nature of the road system such as incidents to avoid, weather hazards (such as flooding), construction and detours, as well as active safety concerns (e.g. - riots) is important for AV operators. Such information cannot be directly sensed from a vehicle's sensor array, but instead must be communicated in a timely and trustworthy channel. Municipalities are interested in pushing this information to AV operators to support emergency response efforts, reduce traffic in construction zones, and generally improve operation of the system. Similarly, information on vehicle safety such as disengagements, as well as critical information on the use of roadway system (trips, origin and destination patterns) are important performance factors for municipalities to understand utilization and plan for appropriate infrastructure. As mobility shifts to on-demand options, the need for safe and coordinated pick-up and drop-off zones will increase (potentially reducing parking needs). For all of these reasons, communication flows between AV operators and municipalities are becoming increasingly important. This paper investigates the functions, emerging practices and protocols for sharing of such critical data, and identifies gaps in and challenges in existing practices. Additionally, case studies are used to highlight the impacts of data sharing between AV operators and municipalities.
Solid state data storage digital telemetry system which makes it possible to store and retransmit after reentry blackout
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.
A hybrid flush/synthetic air data sensing filter for entry vehicle atmosphere estimation is developed. The approach makes use of a Kalman-Schmidt and Rauch-Tung-Streibel smoother. The filter/smoother blends information from pressure sensors distributed on the heatshield with pseudo-measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars 2020 entry, descent, and landing at Jezero crater on February 18th, 2021. The final paper will also include results from the Mars Science Laboratory entry, descent, and landing.
This dataset provides the following on road testing data: - Videos - In-vehicle dash camera videos during different testing scenarios. - Signal controller data - NTCIP log data and processed signal timing data from the corresponding signal controllers - Vehicle data - Vehicle data recorded during the testing, including GNSS, communication, CAN signals.
This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.
Shuttle orbiter vehicle flight data obtained during operation of the reaction control subsystem (RCS) were compared with predictions derived from RCS wind tunnel test data. This paper reviews the derivation of the wind tunnel data base and discusses how it was used to predict the full-scale RCS effects. Flight and predicted data comparisons include the lateral and directional aerodynamic effects of firing the side jets, longitudinal aerodynamics for pitch jets, and lateral aerodynamics for roll jets. Flight data anomalies resulting from wind tunnel limitations in representing portions of the entry flight trajectory are presented. The cause of each data anomaly is described, as well as a requirement for additional technical analysis to establish RCS effect simulation parameters that can be used to update jet effect technology.
This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. 
As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.
The consolidated instrumentation plan, for employing optical and electronic data acquisition systems to monitor the performance and trajectory of Apollo Saturn 5 vehicle 511 during the launch phase of the mission (prelaunch, liftoff to insertion), is presented. Telemetry, optical, and electronic tracking equipment on board the vehicle and data acquisition systems monitoring the flight are discussed. Flight safety instrumentation, vehicle data transmission systems, and geophysical instrumentation are also described.
The consolidated instrumentation plan is presented for employing optical and electronic data acquisition systems to monitor the performance and trajectory of Skylab 1 and Skylab 2 vehicles during the launch phase. Telemetry, optical, and electronic tracking equipment on board the vehicles, and data acquisition systems monitoring the flights are discussed. Flight safety instrumentation, vehicle data transmission systems, and instrumentation geography are also described.
Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.
A hybrid flush/synthetic air data sensing filter utilizing Kalman-Schmidt and Rach-Tung-Striebel smoothers is developed to obtain entry vehicle atmosphere estimates. The filter/smoother blends information from pressure sensors distributed on the heatshield with measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars Science Laboratory and Mars 2020 entry, descent, and landing at Gale crater and at Jezero crater, respectively. The results show that the hybrid filter produces estimates of the freestream flight condition with lower uncertainty than either the flush or synthetic air data algorithms. The filter accomplishes this result by incorporating additional data and computing estimates of systematic error parameters in the pressure data and the aerodynamic model to further reduce the uncertainties.