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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 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRS2004

Fleet Assignment Using Collective Intelligence

Product distribution theory is a new collective intelligence-based framework for analyzing and controlling distributed systems. Its usefulness in distributed stochastic optimization is illustrated here through an airline fleet assignment problem. This problem involves the allocation of aircraft to a set of flights legs in order to meet passenger demand, while satisfying a variety of linear and non-linear constraints. Over the course of the day, the routing of each aircraft is determined in order to minimize the number of required flights for a given fleet. The associated flow continuity and aircraft count constraints have led researchers to focus on obtaining quasi-optimal solutions, especially at larger scales. In this paper, the authors propose the application of this new stochastic optimization algorithm to a non-linear objective cold start fleet assignment problem. Results show that the optimizer can successfully solve such highly-constrained problems (130 variables, 184 constraints).

Antoine, Nicolas E.↗

NASA NTRS2009

Designing of a Fleet-Leader Program for Carbon Composite Overwrapped Pressure Vessels

Composite Overwrapped Pressure Vessels (COPVs) are often used for storing pressurant gases on board spacecraft when mass saving is a prime requirement. Substantial weight savings can be achieved compared to all metallic pressure vessels. For example, on the space shuttle, replacement of all metallic pressure vessels with Kevlar COPVs resulted in a weight savings of about 30 percent. Mass critical space applications such as the Ares and Orion vehicles are currently being planned to use as many COPVs as possible in place of all-metallic pressure vessels to minimize the overall mass of the vehicle. Due to the fact that overwraps are subjected to sustained loads during long periods of a mission, stress rupture failure is a major concern. It is, therefore, important to ascertain the reliability of these vessels by analysis, since it is practically impossible to show by experimental testing the reliability of flight quality vessels. Also, it is a common practice to set aside flight quality vessels as "fleet leaders" in a test program where these vessels are subjected to slightly accelerated operating conditions so that they lead the actual flight vessels both in time and load. The intention of fleet leaders is to provide advanced warning if there is a serious design flaw in the vessels so that a major disaster in the flight vessels can be averted with advance warning. On the other hand, the accelerating conditions must be not so severe as to be prone to false alarms. The primary focus of the present paper is to provide an analytical basis for designing a viable fleet leader program for carbon COPVs. The analysis is based on a stress rupture behavior model incorporating Weibull statistics and power-law sensitivity of life to fiber stress level.

Murthy, Pappu L.N.↗

NASA NTRS2014

The Fleet Application for Scheduling and Tracking (FAST) Management Website

The FAST application was designed to replace the paper and pen method of checking out and checking in GSA Vehicles at KSC. By innovating from a paper and pen based checkout system to a fully digital one, not only the resources wasted by printing the checkout forms have been reduced, but it also reduces significantly the time that users and fleet managers need to interact with the system as well as improving the record accuracy for each vehicle. The vehicle information is pulled from a centralized database server in the SPSDL. In an attempt to add a new feature to the FAST application, the author of this report (alongside the FAST developers) has been designing and developing the FAST Management Website. The GSA fleet managers had to rely on the FAST developers in order to add new vehicles, edit vehicles and previous transactions, or for generating vehicles reports. By providing an easy-to-use FAST Management Website portal, the GSA fleet managers are now able to easily move vehicles, edit records, and print reports.

Object-Oriented Programming↗

NASA NTRS2015

Precision of FLEET Velocimetry Using High-Speed CMOS Camera Systems

Femtosecond laser electronic excitation tagging (FLEET) is an optical measurement technique that permits quantitative velocimetry of unseeded air or nitrogen using a single laser and a single camera. In this paper, we seek to determine the fundamental precision of the FLEET technique using high-speed complementary metal-oxide semiconductor (CMOS) cameras. Also, we compare the performance of several different high-speed CMOS camera systems for acquiring FLEET velocimetry data in air and nitrogen free-jet flows. The precision was defined as the standard deviation of a set of several hundred single-shot velocity measurements. Methods of enhancing the precision of the measurement were explored such as digital binning (similar in concept to on-sensor binning, but done in post-processing), row-wise digital binning of the signal in adjacent pixels and increasing the time delay between successive exposures. These techniques generally improved precision; however, binning provided the greatest improvement to the un-intensified camera systems which had low signal-to-noise ratio. When binning row-wise by 8 pixels (about the thickness of the tagged region) and using an inter-frame delay of 65 microseconds, precisions of 0.5 meters per second in air and 0.2 meters per second in nitrogen were achieved. The camera comparison included a pco.dimax HD, a LaVision Imager scientific CMOS (sCMOS) and a Photron FASTCAM SA-X2, along with a two-stage LaVision HighSpeed IRO intensifier. Excluding the LaVision Imager sCMOS, the cameras were tested with and without intensification and with both short and long inter-frame delays. Use of intensification and longer inter-frame delay generally improved precision. Overall, the Photron FASTCAM SA-X2 exhibited the best performance in terms of greatest precision and highest signal-to-noise ratio primarily because it had the largest pixels.

Peters, Christopher J.↗

NASA NTRS2015

Precision of FLEET Velocimetry Using High-speed CMOS Camera Systems

Femtosecond laser electronic excitation tagging (FLEET) is an optical measurement technique that permits quantitative velocimetry of unseeded air or nitrogen using a single laser and a single camera. In this paper, we seek to determine the fundamental precision of the FLEET technique using high-speed complementary metal-oxide semiconductor (CMOS) cameras. Also, we compare the performance of several different high-speed CMOS camera systems for acquiring FLEET velocimetry data in air and nitrogen free-jet flows. The precision was defined as the standard deviation of a set of several hundred single-shot velocity measurements. Methods of enhancing the precision of the measurement were explored such as digital binning (similar in concept to on-sensor binning, but done in post-processing), row-wise digital binning of the signal in adjacent pixels and increasing the time delay between successive exposures. These techniques generally improved precision; however, binning provided the greatest improvement to the un-intensified camera systems which had low signal-to-noise ratio. When binning row-wise by 8 pixels (about the thickness of the tagged region) and using an inter-frame delay of 65 micro sec, precisions of 0.5 m/s in air and 0.2 m/s in nitrogen were achieved. The camera comparison included a pco.dimax HD, a LaVision Imager scientific CMOS (sCMOS) and a Photron FASTCAM SA-X2, along with a two-stage LaVision High Speed IRO intensifier. Excluding the LaVision Imager sCMOS, the cameras were tested with and without intensification and with both short and long inter-frame delays. Use of intensification and longer inter-frame delay generally improved precision. Overall, the Photron FASTCAM SA-X2 exhibited the best performance in terms of greatest precision and highest signal-to-noise ratio primarily because it had the largest pixels.

Peters, Christopher J.↗

DOE OSTI2015

CNG and Fleets: Building Your Business Case

Two online resources help fleets evaluate the economic soundness of a compressed natural gas program. The National Renewable Energy Laboratory's (NREL's) Vehicle Infrastructure and Cash-Flow Evaluation (VICE 2.0) model and the accompanying report, Building a Business Case for Compressed Natural Gas in Fleet Applications, are uniquely designed for fleet managers considering an investment in CNG and can help ensure wise investment decisions about CNG vehicles and infrastructure.

03 NATURAL GAS↗

DOE OSTI2015

Data Driven Fuel Efficient Driving Behavior Feedback for Fleet Vehicles

Dependency of the transport sector on fossil fuels is encouraging a significant amount of research in to improving fuel efficiency in vehicles. Three primary techniques are identified for vehicle fuel efficiency improvement: 1) vehicle technology improvements such as drivetrain improvements, 2) traffic infrastructure improvements such as traffic flow management and route selection, and 3) driver behavior changes such as acceleration and deceleration profiles. Out of the 3 techniques, driver behavior changing has the least implementation cost and is able to provide immediate results. Thus, this paper presents a fuel efficient driving behavior identification and feedback architecture that is specific to fleet vehicles. The presented method utilizes historical data from fleet drivers on specific routes and generates fuel optimal velocity profiles that do not affect travel time. The identified velocity profile is the prompted to the driver via a low-cost plug-and-play style un-obstructive display. The display uses an intuitive and easily understandable visualization to prompt drivers on fuel efficient velocity. The presented architecture was tested on the Idaho National Laboratory (INL) bus fleet in real-world driving conditions and was shown to be able to increase the fuel economy by 9% and 20% in two different driving scenarios.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Technology Maintenance Readiness Guide for Zero-Emission Buses: A Guide for Bus Fleet Operators

The National Renewable Energy Laboratory has developed a technology maintenance readiness level (TMRL) guide for advanced technology vehicle fleets to help fleet operators (such as transit agencies) assess their readiness level to maintain new zero-emission buses. This guide mirrors a technology readiness level guide, but it focuses on the ability of the fleet operator to maintain and repair the advanced vehicle technology rather than defining the commercial readiness of the technology itself.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2019

Feasibility Analysis of Taxi Fleet Electrification

Ride-hailing activity is rapidly increasing, largely due to the growth of transportation network companies such as Uber and Lyft. However, traditional taxi companies continue to represent an important mobility option for travelers. Columbus Yellow Cab, a taxi company in Columbus, Ohio, offers traditional line-of-sight hailing as well as digital hailing through a mobile app. Data from Columbus Yellow Cab were provided to the National Renewable Energy Laboratory to analyze the potential for taxi electrification. Columbus Yellow Cab data contained information describing both global positioning system trajectories and taxi meter information. The data spanned a period of 13 months, containing approximately 70 million global system positioning system points, 840 thousand trips, and 170 unique vehicles. A variety of scenarios were evaluated using Columbus Yellow Cab data and the Electric Vehicle Infrastructure Projection Tool (EVI-Pro) to understand challenges and opportunities associated with operating an electrified taxi fleet. Two main factors, access to home charging and vehicle specifications, are shown to be major variables affecting successful electric fleet operation. The analysis indicates that 95.7% of taxi travel days can be successfully completed by a 250-mile-range electric vehicle assuming access to overnight and public charging infrastructure. However, when no overnight access is available to fleet vehicles, only 39.9% of taxi travel days are possible with 250-mile range electric vehicles. An additional scenario, reducing the vehicle range from 250 miles to 100 miles (while controlling for infrastructure access and permitting overnight charging) resulted in only 34.4% of taxi travel days being completed.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks

Parallel training of an ensemble of Deep Neural Networks (DNN) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task. Prior efforts have shown that data sharing, where the common preprocessing operation is shared across the DNN training pipelines, saves computational resources and improves pipeline efficiency. Data sharing strategy, however, performs poorly for a heterogeneous set of DNNs where each DNN has varying computational needs and thus different training rate and convergence speed. This paper proposes FLEET, a flexible ensemble DNN training framework for efficiently training a heterogeneous set of DNNs. We build FLEET via several technical innovations. We theoretically prove that an optimal resource allocation is NP-hard and propose a greedy algorithm to efficiently allocate resources for training each DNN with data sharing. We integrate data-parallel DNN training into ensemble training to mitigate the differences in training rates and introduce checkpointing into this context to address the issue of different convergence speeds. Experiments show that FLEET significantly improves the training efficiency of DNN ensembles without compromising the quality of the result.

Guan, Hui↗

DOE OSTI2015

Electric Vehicle Preparedness: Task 1, Assessment of Fleet Inventory for Marine Corps Base Camp Lejeune

Several U.S. Department of Defense-based studies were conducted to identify potential U.S. Department of Defense transportation systems that are strong candidates for introduction or expansion of plug-in electric vehicles (PEVs). Task 1 included a survey of the inventory of non-tactical fleet vehicles at the Marine Corps Base Camp Lejeune (MCBCL) to characterize the fleet. This information and characterization will be used to select vehicles for monitoring that takes place during Task 2. This monitoring involves data logging of vehicle operation in order to identify the vehicle’s mission and travel requirements. Individual observations of these selected vehicles provide the basis for recommendations related to PEV adoption. It also identifies whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements and provides observations related to placement of PEV charging infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Fleet Compliance Annual Report: Model Year 2016, Fiscal Year 2017

This annual report of the Alternative Fuel Transportation Program, which ensures compliance with DOE regulations covering state government and alternative fuel provider fleets pursuant to the Energy Policy Act of 1992 (EPAct), as amended, provides fleet compliance results for manufacturing year 2016 / fiscal year 2017.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Fleet Compliance Annual Report: Model Year 2017, Fiscal Year 2018

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Fleet Efficiency and Electrification Technical Assistance and Tools

Private enterprises, transit agencies, and governments around the world rely on NREL to evaluate their fleet electrification needs. From testing vehicle charging equipment in the lab, to working with fleets to adopt new types of vehicles, to helping agencies develop electric vehicle (EV) plans, NREL transportation analysts address a range of electrification challenges.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Optimizing Vehicle Fleet and Assignment for Concentrating Solar Power Plant Heliostat Washing

Concentrating solar power central-receiver plants use thousands of sun-tracking mirrors, i.e., heliostats, to reflect sunlight to a central receiver, which collects and uses the heat to generate electricity. Over time, soiling reduces the reflectivity of the heliostats and, therefore, the efficiency of the system. Current industry practice sends vehicles to wash heliostats in an ad hoc fashion. We present a mixed-integer nonlinear program that determines wash vehicle fleet size, mix, and assignment of wash crews to heliostats to minimize the sum of (i) the revenues lost due to heliostat soiling, (ii) the costs of hiring wash crews and operating the vehicles, and (iii) the costs of purchasing wash vehicles. We establish conditions for convexity of the objective function, and then propose a decomposition method that enables near-optimal solutions to the wash vehicle fleet sizing and assignment problem on the order of a couple of minutes. Furthermore, these solutions yield hundreds of thousands of dollars in savings per year over current industry practices.

14 SOLAR ENERGY↗

DOE OSTI2022

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 will be shared by API; a small sample of the vehicle and logger data has been extracted and is available for download while the API is being developed.)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

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↗

DOE OSTI2014

AVTA federal fleet PEV readiness data logging and characterization study for the National Park Service: Grand Canyon National Park

This report focuses on the Grand Canyon National Park (GCNP) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗