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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 sources NASA NTRS DOE OSTI

DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

DOE EV Data Collection - Maintenance Data

Maintenance data includes information on maintenance performed on the electric vehicles, including preventive maintenance, service calls, and availability of the vehicles. The parameters collected, and their definitions, will vary due to the differences in maintenance tracking systems that exist between fleets. Parameter definitions are detailed in the data dictionary, and specific vehicle information is available in the vehicle attributes table. Vehicle ID can be used as a key between maintenance data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

DOE EV Data Collection - Charging Data

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The **Charge Data Daily/Session Dictionaries** contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The **Charger Attributes** table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The **Charger Attributes Data Dictionary** contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The **Charging Data** tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

DOE EV Data Collection - Facility Data

Facility data includes information on electricity consumption by larger-scale infrastructure, including buildings, solar arrays, and energy storage systems. Parameter definitions can be found in the data dictionary. If a connection between specific vehicle information and facility data exists, it will be available in the vehicle attributes table. Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Energy Consumption, Performance, and Cost Estimates for Medium and Heavy-Duty Vehicles Based on 2022 Assumptions

Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗

DOE OSTI2026

Fleet Size Requirements for Each GSE Type at Each Airport

This dataset contains the required number of vehicles for each GSE type at each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6). ![image](gse-vehicles-chargers.png) Number of GSE vehicles per GSE type and chargers required for different airport categories under charging scenario 1.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

220EV Box Truck Telemetry Dataset

This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Detailed Simulation Datasets Quantifying U.S. DOE VTO/HFTO R&D Benefits Across Light- to Heavy-Duty Vehicles

For more than 20 years, Argonne National Laboratory’s Vehicle & Mobility Systems Department has assessed how R&D investments by the U.S. Department of Energy’s Transportation Technologies Office and Alternative Fuels and Feedstocks Office affect vehicle energy use and cost. The analyses are performed using Autonomie, Argonne’s full-vehicle simulation tool for energy consumption, performance, and cost. The study covers five time frames ranging from present day through 2050, with more than 30 vehicle classes and applications (10 light duty and >20 medium and heavy duty), as well as six powertrain configurations (conventional, start-stop, hybrid electric vehicle, plug-in hybrid electric vehicle, battery-electric vehicle, and fuel cell electric vehicle) and five fuels (gasoline, diesel, natural gas, hydrogen, and electricity). Low and high technology uncertainty scenarios have been considered to capture a realistic range of outcomes. The resulting datasets include the assumptions used (i.e., efficiency, $/kWh), vehicle-level data (power, energy, weight, and cost), and outputs such as energy consumption, manufacturer’s suggested retail price, and total cost of ownership. These data are critical to stakeholders working in transportation, technology assessment, and long-term R&D planning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research

Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

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 OSTI2026

National Summary Statistics for Depot-Based Medium- and Heavy-Duty Vehicle Operations

This file provides nationally aggregated summary statistics to characterize the daily operations of medium- and heavy-duty vehicles. It provides typical daily driving distances, domicile dwell durations, and one version of potential normalized electric vehicle depot charging load curves. The methodology to create these data was designed with national representativeness in mind, and the data are suitable for national analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

FleetREDI Dashboard Fleet DNA Data Summaries

Developing daily duty cycle summaries for every vehicle-day within NLR’s Fleet DNA database was a key output of the FleetREDI project. This project captured second-by-second GPS and controller area network (CAN) data on in-use medium- and heavy-duty fleet vehicles and then summarized the data to provide an overview of vehicle operation throughout the United States. These data summaries were then displayed in aggregated formats on the FleetREDI dashboard, where users can explore the data within Fleet DNA. Fleet DNA’s clearinghouse of commercial fleet vehicle operating data helps vehicle manufacturers and developers optimize vehicle designs and helps fleet managers choose advanced technologies for their fleets. This online tool, which provides data summaries and visualizations similar to real-world "genetics" for medium- and heavy-duty fleet vehicles, helps users understand the broad operational range of commercial vehicles across vocations and weight classes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset of U.S. School Bus Depots

A large body of public health literature describes how undesirable or dangerous facilities, such as truck depots and industrial plants, located in or near communities can lead to health harms. Research also describes the high levels of traffic-related air and noise pollution that is linked to health harms and may be disproportionately distributed near many schools. Therefore, a primary use case for this dataset is to analyze the location of school bus depots and to create an evidence base that would better enable the work of community members, advocates, and other stakeholders toward improving air quality and public health. Other possible uses for this school bus depot dataset include electricity grid planning and reliability, given recent momentum toward school bus electrification. This dataset was created using an object-based approach with remote sensing data. The primary source of aerial imagery was the National Agriculture Imagery Program (NAIP) dataset. NAIP imagery was analyzed to locate individual school buses based on their color and size, and then classified clusters of school buses as potential depots, which were then verified visually. The resulting dataset contains 11,309 depots across the 48 contiguous U.S. states and Washington, D.C. Fifty-one percent (5,730 depots) are at schools, defined as being 350 meters or less from the nearest school. The accuracy of the dataset was assessed by comparing it with independent reference datasets containing 506 depots from the records of two school transportation companies. We found good agreement, with an omission error rate of 15.2% (77 depots). This dataset represents one of the only remote sensing projects to conduct object detection using data at the sub-meter to 1-meter resolution for a continental-scale application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

Capturing real-world data is critical to reducing the barriers to technology advancement for commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores propane and diesel Class 6 mail delivery trucks. Propane-powered commercial vehicles can achieve the same duty cycles and match the power, acceleration, and cruising speeds of conventionally fueled vehicles. The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a cost-effective alternative to conventional diesel trucks. ![FleetREDI clean delivery trucks](FleetREDI-clean-delivery.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

FleetREDI: Mail Delivery Class 6 Propane Trucks

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Innovating High Throughput Hydrogen Stations: Cooperative Research and Development Final Report, CRADA Number CRD-18-00773

Hydrogen stations today serve the emerging market of light duty fuel cell vehicles, primarily in California with over 30 public retail locations. There has been a steady increase in the number of stations open and hydrogen dispensed, especially in the last two years. From 2015 to 2016, the annual amount of hydrogen dispensed increased from 27,400 kg to 109,200 kg, a nearly fourfold increase in just one year. One station dispensed nearly 12,000 kg in the second quarter of 2017. Despite the significant progress, gaps exist between current infrastructure capabilities and future requirements. For example, fuel cell vehicle applications such as buses, medium-duty, and heavy-duty trucks will gain market share and this must be considered as future customers at hydrogen stations. The expected number of light duty fuel cell vehicles in California alone are expected to grow from approximately 4,000 to over 13,000 by 2020, and 37,000 by 2023. To serve the multiple mobile fuel cell technologies and increased demand, hydrogen stations will have to increase output, decrease cost, and improve reliability. To address these challenges, the project team will demonstrate a hydrogen-focused integrated renewable energy production, storage, and transportation fuel distribution/retailing system. The proposed R&D tasks address key challenges related to light duty station/component reliability and development and validation of high flow rate system models for new applications like medium and heavy-duty truck fueling.

08 HYDROGEN↗

DOE OSTI2026

Municipal Fleet Action Plan Guide

This action plan template and guide have been designed to support light-duty or medium-duty municipal fleet managers as they plan to adopt electric vehicles. The resource is an adaptation of content created by the National Laboratory of the Rockies (NLR), with support from World Resources Institute (WRI), for participants in the U.S. Department of Energy's Energy to Communities peer-learning cohort, Charting a Path to Municipal Fleet Electrification. Fifteen participating municipalities joined monthly cohort workshops from July through December 2024 to learn and plan for their own fleet electrification. Clean Cities and Communities coalitions supported participating municipalities throughout the cohort series by conducting fleet analysis and planning activities on their behalf. This template is an adapted version of cohort activities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗