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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 145 records · Page 8

High-Mileage Courier Fleet Vehicle On-Road Logger Data

This dataset describes the performance and fuel efficiency of AVTA test vehicles operating in commercial courier fleets the Phoenix, AZ metro area between 2010 and 2016. Aftermarket data loggers were installed in two to four vehicles of each of 30+ distinct year/make/models (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Loggers recorded vehicle operation as they were driven up to 160,000 miles in up to three years of fleet testing. Parameters were logged at 1-second intervals, including - vehicle speed, - engine and/or electric motor speed, - fuel and/or electricity consumption, and - ambient temperature. This dataset includes both raw second-by-second data and trip-level metrics. This dataset is shared by API; a small sample of the vehicle and logger data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

FEDFLEET: FY 2020 Fleet Data Submissions Status Update [Slides]

This presentation presents a brief overview of the current status of the collection of federal motor vehicle fleet data for fiscal year (FY) 2020 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 covers current status of data collection for FY 2020, the review and feedback process for those submissions, timeline of finalizing the information, and a preliminary high-level overview of the information.

99 GENERAL AND MISCELLANEOUS↗

FY 2020 Federal Fleet Data Submissions: Status Update [PowerPoint]

This presentation presents a brief overview of the current status of the collection of federal motor vehicle fleet data for fiscal year (FY) 2020 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 covers current status of data collection for FY 2020, the review and feedback process for those submissions, timeline of finalizing the information, and a preliminary high-level overview of the information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Ontario International Airport Fleet Electrification Blueprint: Zero-Emission Vehicle Technology Assessment for Energy Optimization [Slides]

Ontario International Airport Authority (OIAA)'s Electric Vehicle (EV) Blueprint project presentation focuses on the possibilities for fleet electrification, including medium heavy duty charging and hydrogen refueling and their associated planning, infrastructure, operation, and maintenance options. This report includes detailed list of available electric ground support equipment (GSE), description of NREL tools for EV infrastructure design and optimization, and analyses of the current fleet and the potential grid impacts of electrification. Key recommendations are made in regard to simulation-based and in-depth system analyses to identify optimal charging infrastructure and long-term grid stability, and to ensure that the selected station architecture will provide enough space and capacity for future expansion and resiliency.

30 DIRECT ENERGY CONVERSION↗

Fleet Electrification Framework [Slides]

The ZEV Ready Center provides a step-by-step process to plan for fleet electrification, design charging infrastructure, and support vehicle operators. This presentation describes how the ZEV Ready Center can be used by local communities and focuses particularly on the ZEV Planning and Charging (ZPAC) tool to identify good candidates for fleet electrification and charging station requirements.

33 ADVANCED PROPULSION SYSTEMS↗

PV Fleet Performance Data Initiative Final Technical Report (FTR)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. This project will leverage data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Please see our list of public reports at https://www.nrel.gov/pv/fleet-performance-data-initiative.html. Objective 1: Support the global PV industry with scalable, robust data analysis tools that reduce the uncertainty of PV system performance and loss calculation. Objective 2: Reduce perceived risk arising from degradation rate, soiling loss, and system availability by publishing detailed statistics on U.S. fleet performance. Objective 3: Highlight factors leading to system underperformance including module type, climate, mounting configuration, etc. Objective 4: Enable continued high system performance in modern PV systems, as turnover and advances in technology bring new suppliers and high-efficiency modules into the market.

14 SOLAR ENERGY↗

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

25 ENERGY STORAGE↗

Modeled Electricity Demand Profiles for Federal, State, and Municipal Electric Vehicle Fleets in the United States

Federal, state, and municipal electric vehicle fleet hourly load datasets at the Uber H3 hex, county, and city resolutions, as described in Singer et al. (2025). Please cite as: Singer, Mark, Cabell Hodge, Kara Podkaminer, and Brennan Borlaug. 2025. Hourly Load Profile Dataset for Federal, State, and Municipal Electric Vehicle Fleets in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-92142. https://www.nlr.gov/docs/fy25osti/92142.pdf

24 POWER TRANSMISSION AND DISTRIBUTION↗

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (AAM) (i.e. urban and rural unmanned aircraft systems) ecosystem, the NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV) to prototype and study the effectiveness AAM capabilities under various operational contexts. HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called the Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with the FMI to identify optimal approaches for displaying information to human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a remotely controlled vehicle completed a takeoff, active flight, and landing sequence while simulated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self- reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users suggested customizable interfaces to accommodate information display preferences, and the ability to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could serve to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

Fleet manager↗

sUAS Ground Control Station Capabilities Impact on Fleet Management

Future Small Uncrewed Aerial Systems (sUAS) missions will require multiple operators to collaborate and manage large fleets of highly automated vehicles. It is critical that these operators maintain the necessary situational awareness to modify vehicle missions if/when unexpected situations arise in the operating environment. It has been suggested that highly automated vehicle capabilities can lead to reducing the capabilities given to operators to modify/direct vehicle missions. This paper explores various Ground Control Station (GCS) capabilities configurations and their impact on sUAS fleet management. Results show that a GCS with a combination of manual and automated capabilities allowed participants to make more effective decisions while maintaining workload and response times similar to that of a GCS with only automated capabilities.

sUAS↗

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

AAM↗

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

high density vertiport↗

Charging and Repositioning Decision Making for Fully Automated Ride-Hailing Fleet

This is the presentation profile for 2020 DOE Vechicle Technologies Office Annual Merit Review. It introduces the research results in the project "Charging and Repositioning Decision Making for Fully Automated Ride-Hailing Fleet". These results come from the work of developing a framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet, and a case study in New York City was conducted to investigate the benefits of systematic optimization approach comparing to a heuristic approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

EV Grid Integration: Pass Cars and Commercial Fleets

The presentation discusses grid integration of EVs, both light duty vehicles and commercial fleets under these topics - Grid impact analysis for pass cars, Opportunities for smart charging for pass cars, Considerations for commercial fleet electrification, and DER Co-optimization for improved business case.

47 OTHER INSTRUMENTATION↗

Loss Factor Assessment in the 8GW PV Fleet Performance Data Initiative

This presentation is divided into the following sections: (1) photovoltaics (PV) current and future deployment; (2) the PV Fleet Performance Data Initiative; (3) fleet degradation trends; (4) high-efficiency module performance; (5) other system loss factors; and (6) conclusions.

deployment↗

PV Inverter Availability from the U.S. PV Fleet

In the PV Fleet Performance Data Initiative, we partner with photovoltaic (PV) fleet owners to collect time-series PV production data, and publish aggregated, anonymized results. An assessment of system availability is conducted on 1128 systems which passed our data quality checks, and include cumulative energy meter data. Overall inverter availability is low in the first 6 months of system performance before reaching steady-state by the end of the first year. System-level aggregated data shows a median (P50) system availability of 0.99, and a lower P90 value of 0.95. A dependence on system size is also identified, with better inverter availability results for smaller PV systems. Potential causes of this effect may include the selection of inverter itself: smaller inverters 6kW-250kW showed better average availability than inverters 300kW-5MW. The elimination of string combiner boxes and lower energy impact when one particular inverter goes off-line are potential benefits of a string inverter-based PV system architecture. DNV also analyzed availability data from over 1100 operating systems and found similar trends. DNV's P50 industry guidance on expected availability has been updated to reflect the data and the following observations: utility scale systems have lower availability than DG systems, availability is lower in first year compared to subsequent years, and that actual availability is lower than expected.

fleet↗

Photovoltaic fleet degradation insights

Abstract In the PV Fleet Performance Data Initiative, high‐frequency data from commercial and utility‐scale photovoltaic (PV) systems have been collected to examine performance loss rates (PLRs) at a fleet scale. To date, performance data from more than 7.2‐gigawatt (GW) capacity, 1700 sites and 19,000 inverters—approximately equivalent to 6% to 7% of the entire US PV market—have been collected. An overall PLR of −0.75%/year was found, which is in line with historical and recent findings. Tracked silicon (Si) and cadmium telluride (CdTe) performed comparably with all fixed‐tilt systems. Higher PLRs were found for hotter temperature zones; cooler climates exhibit a median −0.48%/year loss, which increases to −0.88%/year in hotter climates. High‐efficiency module technologies showed median PLRs in line with conventional Si technologies but demonstrated markedly different PLR behavior when filtered only for low‐light conditions <600 W/m 2 . Causes for this technology‐dependent behavior are under investigation.

14 SOLAR ENERGY↗

Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling

Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers and lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic pooling algorithm was introduced to match SAVs with riders while coordinating the riders' arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a dynamic pooling algorithm tightly coordinated with train arrivals (every 15 min) delivers 87% of travelers to their stations in time to catch the next train, whereas uncoordinated assignments deliver just 58% of travelers in time.

33 ADVANCED PROPULSION SYSTEMS↗