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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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All sourcesNASA NTRSDOE OSTI
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 OSTI2020

FAST: Continuing the Focus on Data Quality

This presentation provides an overview of fiscal year 2019 federal motor vehicle fleet data, collected at the individual vehicle level during the fall of 2019, how the the collecting project has reviewed that information for potential quality issues, how the quality of this year's data submission compare to the prior year, and recommendations for federal agencies in their efforts to continue to improve the quality of their submissions. This presentation will be given at the January 2020 FedFleet training event, hosted by the US General Services Administration in Washington, DC. The information is collected through the Federal Automotive Statistical Tool (FAST) project. FAST is a Web-based information system managed by the US Department of Energy, the US General Services Administration, and the Energy Information Administration. FAST is used to collect information about the fleet of motor vehicles used and managed by the Federal government. FAST is developed and maintained by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Downloadable Dynamometer Database (D3): Public Test Data on Advanced-Technology Vehicles

Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

FedFleet 2022: Federal Automotive Statistical Tool - FY 2021 Fleet Trends and Data Quality

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2021, discusses quality of the submitted data, and touches on future aspects of fleet data collection and reporting. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Federal Automotive Statistical Tool: FY 2022 Federal Fleet Trends and Data Quality [Slides]

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2022 and discusses quality of the submitted data. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

FedFleet 2021: Federal Automotive Statistical Tool - Federal Vehicle Fleet Data Collection

This presentation presents a brief overview of the collection of information about the US government's fleet of motor vehicles using the Federal Automotive Statistical Tool (FAST), discusses the makeup and operation of the vehicle fleet during FY 2020, discusses challenges associated with quality of the submitted data, and touches on future aspects of fleet data collection and reporting. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Data Quality Assessment Process for Real-Time Data-Driven Traffic Microsimulation of Smart Corridor

Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2024

FedFleet 2024: Fleet Data Quality - Understanding and Improving Your Agency's FAST Fleet Data Quality Metric? [Slides]

This presentation presents an overview of how the team supporting the Federal Automotive Statistical Tool (FAST) project for DOE and GSA view and assess the quality of fleet data submissions from federal agencies. It also discusses ways in which agencies can improve the quality of their fleet data and of their fleet data submissions. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2024

Assessing Resilience in Lane Detection Methods: Infrastructure-Based Sensors and Traditional Approaches for Autonomous Vehicles

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. We address this need by evaluating the influence of hazards on the resilience of three different lane detection methods in simulation: (1) traditional camera detection using a U-Net algorithm, (2) radar detections using infrastructure-based radar retro-reflectors (RRs), and (3) direct communication of lane line information using chip-enabled raised pavement markers (CERPMs). The performance of each of these methods is assessed using resilience engineering metrics by simulating the individual methods for each sensor technology’s response to related hazards in the CARLA simulator. Using simulation techniques to replicate these methods and hazards acquires extensive datasets without lengthy time investments. Specifically, the resilience triangle was used to quantitatively measure the resilience of the lane detection system to obtain unique insights into each of the three lane detection methods; notably the infrastructure-based CERPMs and RRs had high resistance to hazards and were not as easily affected as the vision-based U-Net. However, while U-Net was able to recover the fastest from the disruption as compared to the other two methods, it also had the most performance loss. Overall, this study demonstrates that while infrastructure-based lane keeping technologies are still in early development, they have great potential as alternatives to traditional ones.

Patil, Pritesh↗
DOE OSTI2022

Federal Automotive Statistical Tool: FY 2022 Data Call Status & Review Tools [Slides]

This presentation presents an overview of current federal vehicle fleet current data collection efforts covering required information submissions about the motor vehicles, fueling centers, and electric vehicle supply equipment (EVSE) inventory through the Federal Automotive Statistical Tool (FAST). The presentation also provides an overview of the capabilities within FAST to assist federal agency users with reviewing and improving the quality of their fleet data submissions. This presentation is intended for delivery via WebEx at the November 9, 2022 meeting of the DOE-sponsored INTERFUEL working group. FAST is a web-based information management tool developed by INL and funded by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

PHEV Distribution Grid Integration and Smart Systems Testing: Cooperative Research and Development Final Report

To continue NREL's mission, which includes preparing technologies and markets with "speed and scale," NREL researchers will collaborate with Toyota on plug-in electric vehicle technology. Toyota will provide a collection of plug-in hybrid electric vehicles ("PHEVs" [and ultimately including PEVs], collectively, the "Vehicles") to conduct infrastructure interaction testing at NREL facilities. NREL will use available resources in the Vehicle Testing and Integration Facility (VTIF), its staff parking garage with numerous commercial Electric Vehicle Supply Equipment ("EVSEs"), and its Energy Systems Integration Facility (ESIF) to safely execute research experiments that highlight potential distribution system power quality challenges related to plug-in electric vehicles. The expected outcome of this project is a better understanding of the correlation of individual vehicle power quality attributes to a system of vehicles on a distribution network. The data collected and the simulations conducted will be used to guide future experiments and project development that aid in the deployment of plug-in electric vehicles ("PEV").

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Decoupling Power Quality Issues in Grid-Microgrid Network Using Microgrid Building Blocks

Microgrids are evolving as promising options to enhance reliability of the connected transmission and distribution systems. Traditional design and deployment of microgrids require significant engineering analysis. However, Microgrid Building Blocks (MBB), consisting of modular blocks that integrate seamlessly to form effective microgrids, are promising technologies to enable faster and broader adoption of microgrids. Back-to-Back converter placed at the point of common coupling of microgrid is an integral part of MBB. This paper presents applications of MBB to decouple power quality issues in grid-microgrid network serving power quality sensitive critical loads such as data centers, new grid-edge technologies such as vehicle-to-grid generation, and emergency condition loads such as electric vehicle charging loads during evacuation prior disaster events. Simulation results show that MBB effectively decouple the power quality issues across networks and allow network with low power quality to transfer high-power quality power to connected networks during emergency conditions.

Acharya, Samrat S. [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2020

Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data

Standard of practice approaches to time series cluster analysis involve careful feature engineering, often utilizing expert input to tune and select features by hand. In many cases, expert input may not be readily available, or there may not yet exist a community consensus on the ideal features for a given application. This paper compares the results of several cluster analysis methods, using both hand selected features and those extracted automatically, when applied to large geospatial time series telematics data from commercial trucking fleets. The impacts of feature selection, dimensionality reduction, and choice of clustering algorithm on the quality of clustering results are explored. Results from this analysis confirm prior results that domain agnostic features are competitive with the hand engineered features with respect to clustering quality metrics. These results also provide new insight into the most successful strategies for identifying structure in large unstructured vehicle telematics data, and suggest that time series clustering using automatic feature extraction can be an effective approach to extract structure from large scale geospatial time series data in cases when hand selected features are not available.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

FY 2021 Federal Vehicle Fleet Data Overview [Slides]

This presentation provides an overview of the fiscal year (FY) 2021 federal motor vehicle fleet dataset collected through the Federal Automotive Statistical Tool (FAST). FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles. The presentation discusses the size of the dataset; provides a high-level look at what the collected information shows about the makeup and operation of the federal motor vehicle fleet during FY 2021 and how that compares with recent years; discusses the process used to review the agency submissions comprising the dataset; and discusses how overall quality of the dataset has been assessed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Real-time and Autonomous Water Quality Monitoring System Based on Remotely Operated Vehicle

Existing water quality monitoring systems near hydropower facilities are limited by the lack of mobility of the sensors’ carrier platform. Most systems use a buoy, a mounting fixture attached to a solid structure, or a human worker, which significantly limits the selection of the sampling sites and poses safety risks during data collection and equipment maintenance. To improve on this technology, we developed an autonomous water quality monitoring system that can operate in dangerous water environments near hydropower facilities for water sampling at multiple locations. The goal is to enable safe, timely, and comprehensive water-quality data collection; maximize power generation revenue with improved operational control; and reduce Federal Energy Regulatory Commission and state water quality monitoring costs for compliance. The system incorporates a remotely operated vehicle as the mobile monitoring platform, a dissolved oxygen sensor for monitoring water quality, a tether management system for automatically winding the tether, a solar mobile docking platform for suppling power to the ROV, and a web-based graphical user interface for data post-processing and visualization. In addition, preliminary field research are presented to demonstrate the system capabilities.

Salalila, Aljon L.↗
DOE OSTI2023

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗