Results for “fleets” — Page 2

NASA NTRS1975

Tug fleet and ground operations schedules and controls. Volume 1: Executive summary

This study presents Tug Fleet and Ground Operations Schedules and Controls plan. This plan was developed and optimized out of a combination of individual Tug program phased subplans, special emphasis studies, contingency analyses and sensitivity analyses. The subplans cover the Tug program phases: (1) Tug operational, (2) Interim Upper Stage (IUS)/Tug fleet utilization, (3) and IUS/Tug payload integration, (4) Tug site activation, (5) IUS/Tug transition, (6) Tug acquisition. Resource requirements (facility, GSE, TSE, software, manpower, logistics) are provided in each subplan, as are appropriate Tug processing flows, active and total IUS and Tug fleet requirements, fleet management and Tug payload integration concepts, facility selection recommendations, site activation and IUS to Tug transition requirements. The impact of operational concepts on Tug acquisition is assessed and the impact of operating Tugs out of KSC and WTR is analyzed and presented showing WTR as a delta. Finally, cost estimates for fleet management and ground operations of the DDT&E and operational phases of the Tug program are given.

Source record↗

NASA NTRS2005

Path Planning Algorithms for the Adaptive Sensor Fleet

The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

Stoneking, Eric↗

NASA NTRS2016

Application of Strategic Planning Process with Fleet Level Analysis Methods

The goal of this work is to quantify and characterize the potential system-wide reduction of fuel consumption and corresponding CO2 emissions, resulting from the introduction of N+2 aircraft technologies and concepts into the fleet. Although NASA goals for this timeframe are referenced against a large twin aisle aircraft we consider their application across all vehicle classes of the commercial aircraft fleet, from regional jets to very large aircraft. In this work the authors describe and discuss the formulation and implementation of the fleet assessment by addressing the main analytical components: forecasting, operations allocation, fleet retirement, fleet replacement, and environmental performance modeling.

Mavris, Dimitri N.↗

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↗

DOE OSTI2024

Submitting a Standard Compliance Annual Report: EPAct State and Alternative Fuel Provider Fleet Program User Guide

State government and alternative fuel provider fleets covered under the State and Alternative Fuel Provider Fleet Program (Program) established pursuant to the Energy Policy Act of 1992 (EPAct) may use the Compliance Reporting Tool to track and report on several compliance activities. These activities include, but are not limited to, completing Standard Compliance annual reports, Alternative Compliance notices of intent, and exemption requests. Covered fleets can access the Compliance Tool through the Program's website at https://epact.energy.gov/users/sign_in. Covered fleet points of contact should bookmark the Compliance Reporting Tool for future access. This user guide addresses how to complete and submit Standard Compliance annual reports, including getting started with reporting, submitting annual reports, submitting exemption requests, and viewing annual reports.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Scalable GPS Data Logging To Support Advanced Fleet Analysis

This highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. National Laboratory of the Rockies researchers developed and demonstrated low-cost, open-source Arduino data loggers with 3D-printed cases that are compatible with global navigational systems and built with components available ubiquitously worldwide, enabling cost-effective collection and analysis of fleet operational data. Validated on an overseas transit bus fleet, NLR analysis showed that, with sufficient charging opportunities, 90% of observed duty cycles could be accomplished by electric buses with no modifications to operations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Siting and sizing of public–private charging stations impacts on household and electric vehicle fleets

To facilitate the provision of electric vehicle charging stations (EVCS) in urban areas, this study investigates the benefits of co-locating fleet-owned chargers with public charging stations to enable construction incentives and cord-sharing cost savings. Shared EVCS can serve charging demand from both user types: private (household) EV owners and those managing fleet vehicles – like shared and fully automated EV (SAEV) fleets. Using POLARIS to simulate all person-travel across the 6-county Austin, Texas region, new EVCS were sited and sized with DC fast-charging (DCFC) plugs to lower operating and construction costs while providing public + private (PP) service across an 81-square-mile core geofence (where 200 SAEVs were active) over 24-hour days. When co-location is permitted, 115 DCFC cords were added to the 23 existing (publicly available) stations to enable SAEVs and household EVs (HHEVs) charging access, within the geofence. Each 250-mile-range SAEV was simulated to travel an average of 330 miles per day, serve over 92 person-trips, and recharge 2.7 times a day (for 2.4 h per session). The new DCFC plugs were primarily added to public EVCS at shopping centers and schools, and in residential settings along freeways. The average plug served 4.8 EVs per day. Most co-located PP EVCS permitted immediate (no-wait) charging, except for 2 stations along freeways that averaged 8 min of wait time to begin charging. In conclusion, the co-location strategy lowered fleet owners’ initial EVCS construction costs by 12 % (thanks to cord-sharing to avoid cord duplication), while reducing SAEV wait times to just 3.1 min (versus 10.7 min if SAEV managers had to build and operate their own EVCS).

EV charging modeling↗

NASA NTRS2009

JSC Case Study: Fleet Experience with E-85 Fuel

JSC has used E-85 as part of an overall strategy to comply with Presidential Executive Order 13423 and the Energy Policy Act. As a Federal fleet, we are required to reduce our petroleum consumption by 2 percent per year, and increase the use of alternative fuels in our vehicles. With the opening of our onsite dispenser in October 2004, JSC became the second federal fleet in Texas and the fifth NASA center to add E-85 fueling capability. JSC has a relatively small number of GSA Flex Fuel fleet vehicles at the present time (we don't include personal vehicles, or other contractor's non-GSA fleet), and there were no reasonably available retail E-85 fuel stations within a 15-minute drive or within five miles (one way). So we decided to install a small 1000 gallon onsite tank and dispenser. It was difficult to obtain a supplier due to our low monthly fuel consumption, and our fuel supplier contract has changed three times in less than five years. We experiences a couple of fuel contamination and quality control issues. JSC obtained good information on E-85 from the National Ethanol Vehicle Coalition (NEVC). We also spoke with Defense Energy Support Center, (DESC), Lawrence Berkeley Laboratory, and US Army Fort Leonard Wood. E-85 is a liquid fuel that is dispensed into our Flexible Fuel Vehicles identically to regular gasoline, so it was easy for our vehicle drivers to make the transition.

Hummel, Kirck↗

NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

DOE OSTI2026

Understanding Advanced Vehicle Technology Adoption Potential in Commercial Fleets Across Major Trucking Sectors

Adopting advanced vehicle technologies, such as battery-electric, hybrid, and hydrogen fuel cell vehicles, can be an effective strategy for reducing fleet owners' operating costs. However, different trucking sectors, such as private and for-hire carriers and short- or long-haul operations, may face unique challenges in adopting those vehicle technologies due to their own operational needs and budget constraints. Current studies on fleet-wide vehicle technology projections frequently overlook such sectoral differences and fail to capture variation in adoption potential across sectors. This study addresses this gap by analyzing the disparities in the total cost of ownership (TCO) and payback period (PBP) among a large and heterogeneous sample of fleet owners. It aims to understand the sectoral differences in the long-term potential for adopting advanced vehicle technologies. Utilizing the 2021 US Vehicle Inventory and Use Survey (US VIUS), which offers data on various commercial vehicle sectors, their operational patterns, and current vehicle assets, this research estimates the TCO and PBP for individual trucks over multiple future years. The results reveal variation in the cost-effectiveness of different vehicle technologies across trucking sectors, as well as the potential technology landscape in both the short and long term. The findings from this study can inform policymakers and practitioners on how to prioritize sectors with lower barriers for advanced vehicle technology adoption and support industries that face challenges in switching to advanced vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Advancing Our Understanding of System Availability through the PV Fleet Performance Data Initiative

The PV Fleet Performance Data Initiative partners with photovoltaic (PV) fleet owners to collect time-series data of PV production data and publishes aggregated anonymized results of system performance metrics. With an extensive dataset drawn from over 2,200 PV systems across the United States, comprising 8.5 GW and 24,000 separate inverter data channels, this initiative aims to ensure that systemic risks in the US PV fleet are detected. The current work explores system availability, revealing a pronounced dependence on time, especially within the initial 6 months of system performance. Following this start-up period, the average system availability stabilizes. Statistical analyses illustrate a median (P5O) monthly availability of 0.991 and a dependence on system size with a negative trend in availability with increasing system size. This finding indicates that larger systems experience lower availability compared to their smaller counterparts.

inverter availability↗

DOE OSTI2026

High-Mileage Courier Fleet Vehicle Laboratory Battery Pack Testing

For one of each of the AVTA's plug-in hybrid electric vehicles, battery electric vehicles, and some hybrid electric vehicles tested in high-mileage courier fleets, the high-voltage traction battery packs were removed from the vehicle and tested at the beginning and end of fleet testing. For some vehicles, batteries also were tested at periodic intervals during fleet testing. Standard reference performance tests were conducted to characterize battery degradation over time. This dataset contains results from two or more rounds of battery tests for 21 distinct year/make/model vehicles (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). Each round of battery testing included the "Static Capacity Test" and the "Hybrid Pulse Power Characterization (HPPC) Test", conducted according to test procedures published in the United States Advanced Battery Consortium ["Battery Test Manual For Power-Assist Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=57), ["Battery Test Manual For Plug-In Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=168), and ["Electric Vehicle Battery Test Procedures Manual"](https://www.uscar.org/commands/files_download.php?files_id=5) prior to the time of testing. These tests were performed by Intertek Testing Services, North America. This dataset is shared by API; a small sample of the vehicle battery and test data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

FLEET and PLIF Velocimetry Within A Mach 10 Hypersonic Air Flow

Femtosecond laser electronic excitation tagging (FLEET) and planar laser-induced fluorescence (PLIF) velocity measurements utilizing molecular tagging velocity (MTV) methods from three recent test campaigns conducted at the 31-in Mach 10 Air Tunnel at the NASA Langley Research Center are highlighted within. The FLEET measurements reported here include the first direct measurement of freestream velocity at this hypersonic wind tunnel facility. Measurement challenges were exasperated by the low gas density of the Mach 10 air freestream (~0.4% of standard temperature and pressure conditions) and even lower gas densities within the hypersonic wake of a 70-degree sphere-cone model. In addition, the hypersonic freestream and very low speed velocities in the wake also tested the measurement dynamic range. To complement the FLEET measurements in the wake of the sphere-cone model, PLIF velocimetry using seeded nitric oxide was also performed. While NO-PLIF velocimetry has been performed at this facility several times by previous researchers, the use of a 1D diffractive optical element for NO-PLIF velocimetry is reported here for the first time. The 1D DOE enabled the generation of up to 75 laser lines simultaneously and improved the spatial extent of the measurement three times compared to previous work. This enabled a wide velocity measurement plane of approximately 130 mm x 130 mm. The velocimetry methods demonstrated here are expected to improve wind tunnel characterization, provide critical data to validate CFD codes, and improve the design of flight vehicles for planetary entry.

FLEET↗

NASA NTRSDate not supplied

FLEET and PLIF Velocimetry Within A Mach 10 Hypersonic Air Flow

Femtosecond laser electronic excitation tagging (FLEET) and planar laser-induced fluorescence (PLIF) velocity measurements utilizing molecular tagging velocity (MTV) methods from three recent test campaigns conducted at the 31-in Mach 10 Air Tunnel at the NASA Langley Research Center are highlighted within. The FLEET measurements reported here include the first direct measurement of freestream velocity at this hypersonic wind tunnel facility. Measurement challenges were exasperated by the low gas density of the Mach 10 air freestream (~0.4% of standard temperature and pressure conditions) and even lower gas densities within the hypersonic wake of a 70-degree sphere-cone model. In addition, the hypersonic freestream and very low speed velocities in the wake also tested the measurement dynamic range. To complement the FLEET measurements in the wake of the sphere-cone model, PLIF velocimetry using seeded nitric oxide was also performed. While NO-PLIF velocimetry has been performed at this facility several times by previous researchers, the use of a 1D diffractive optical element for NO-PLIF velocimetry is reported here for the first time. The 1D DOE enabled the generation of up to 75 laser lines simultaneously and improved the spatial extent of the measurement three times compared to previous work. This enabled a wide velocity measurement plane of approximately 130 mm x 130 mm. The velocimetry methods demonstrated here are expected to improve wind tunnel characterization, provide critical data to validate CFD codes, and improve the design of flight vehicles for planetary entry.

FLEET↗

NASA NTRSDate not supplied

Wake Velocimetry of a Sphere-Cone Model in a Mach 10 Air Freestream using FLEET

Velocity measurements in the wake behind a sphere-cone-shaped vehicle wind tunnel model in a Mach 10 hypersonic air flow using femtosecond laser electronic excitation tagging (FLEET) are reported in this work. The FLEET measurements were performed at 1 kHz using a femtosecond laser centered at 800 nm and an ultrahigh-speed intensified camera system. The results reported here are for a single freestream condition corresponding to approximately Re ∞ /L = 3.6∙10 6 m -1 . The performance of the velocimetry instrument was evaluated in the quiescent test section at conditions relevant to the wake measurements. For velocity measurements in the wake, the FLEET measurement volume was translated to several radial locations from the model centerline in the vertical direction and at a streamwise location corresponding to approximately 12 mm from the payload. Reverse flow with mean velocities ranging from -12 m/s to -48 m/s were observed in the recirculating region of the wake, whereas hypersonic velocities in the range of 1079 m/s to 1183 m/s were observed in the free shear layer. A velocity uncertainty methodology is also outlined and applied for the reported measurements. The velocity data reported in this work is expected to play a significant role in the validation of computational codes modeling the hard-to-predict separated hypersonic wake.

FLEET↗

NASA NTRSDate not supplied

Wake Velocimetry of a Sphere-Cone Model in a Mach 10 Air Freestream using FLEET

Velocity measurements in the wake behind a sphere-cone-shaped vehicle wind tunnel model in a Mach 10 hypersonic air flow using femtosecond laser electronic excitation tagging (FLEET) are reported in this work. The FLEET measurements were performed at 1 kHz using a femtosecond laser centered at 800 nm and an ultrahigh-speed intensified camera system. The results reported here are for a single freestream condition corresponding to approximately Re ∞ /L = 3.6∙10 6 m -1 . The performance of the velocimetry instrument was evaluated in the quiescent test section at conditions relevant to the wake measurements. For velocity measurements in the wake, the FLEET measurement volume was translated to several radial locations from the model centerline in the vertical direction and at a streamwise location corresponding to approximately 12 mm from the payload. Reverse flow with mean velocities ranging from -12 m/s to -48 m/s were observed in the recirculating region of the wake, whereas hypersonic velocities in the range of 1079 m/s to 1183 m/s were observed in the free shear layer. A velocity uncertainty methodology is also outlined and applied for the reported measurements. The velocity data reported in this work is expected to play a significant role in the validation of computational codes modeling the hard-to-predict separated hypersonic wake.

FLEET↗

DOE OSTI2024

Example Alternative Compliance Annual Report: EPAct State and Alternative Fuel Provider Fleet Program User Guide

The U.S. Department of Energy developed an electronic reporting spreadsheet to facilitate fleets' preparation of a complete Alternative Compliance (AC) annual report. All fleets that participate in AC are encouraged to use the spreadsheet. The following examples include one AC annual report that uses the spreadsheet and one AC annual report that does not use the spreadsheet. Both examples include all components that must be included in a fleet's AC annual report. For further instructions on how to use the reporting spreadsheet, review the Alternative Compliance Guidance Document.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2024

Example Alternative Compliance Waiver Application: EPAct State and Alternative Provider Fleet Program Guidance Document

The U.S. Department of Energy developed an electronic planning spreadsheet to facilitate fleets' preparation of a complete Alternative Compliance (AC) waiver application. All fleets that participate in AC are encouraged to use the spreadsheet. The example in this guidance document includes all components that must be included in a fleet’s AC waiver application.

AC waiver↗