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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

In-Use Fleet Evaluation of Fast-Charge Battery Electric Transit Buses

With support from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory (NREL) conducts real-world performance evaluations of advanced medium- and heavy-duty fleet vehicles. Evaluation results can help vehicle manufacturers fine-tune their designs and assist fleet managers in selecting fuel-efficient, low-emission vehicles that meet their economic and operational goals. In 2015, NREL launched an in-service evaluation of 12 battery electric buses (BEBs) compared to conventional compressed natural gas (CNG) buses operated by Foothill Transit in West Covina, California. The study aims to improve understanding of the overall usage and effectiveness of fast-charge BEBs and associated charging infrastructure in transit operation. To date, NREL researchers have analyzed more than 148,000 km of in-use operational data, including driving and charging events. Foothill Transit purchased the BEBs with grant funding from the Federal Transit Administration's Transit Investments for Greenhouse Gas and Energy Reduction Program.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

FAST FY 2018 Federal Fleet Data Call: Looking at Data Quality

This presentation provides an overview of fiscal year 2018 federal motor vehicle fleet data at the individual vehicle level during the fall of 2018, a preliminary summary of the data collected, how the the collecting project has reviewed that information for potential quality issues, and an overview of two key areas within the dataset that represent data quality issues. This presentation will be given at the April 2019 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 OSTI2020

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↗
DOE OSTI2020

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↗
DOE OSTI2022

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↗
DOE OSTI2024

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↗
DOE OSTI2017

Measured and estimated performance of a fleet of shaded photovoltaic systems with string and module-level inverters

Shade obstructions can significantly impact the performance of photovoltaic (PV) systems. Although there are many models for partially shaded PV arrays, there is a lack of information available regarding their accuracy and uncertainty when compared with actual field performance. This work assesses the recorded performance of 46 residential PV systems, equipped with either string-level or module-level inverters, under a variety of shading conditions. We compare their energy production data to annual PV performance predictions, with a focus on the practical models developed here for National Renewable Energy Laboratory's system advisor model software. This includes assessment of shade extent on each PV system by using traditional onsite surveys and newer 3D obstruction modelling. The electrical impact of shade is modelled by either a nonlinear performance model or assumption of linear impact with shade extent, depending on the inverter type. When applied to the fleet of residential PV systems, performance is predicted with median annual bias errors of 2.5% or less, for systems with up to 20% estimated shading loss. The partial shade models are not found to add appreciable uncertainty to annual predictions of energy production for this fleet of systems but do introduce a monthly root-mean-square error of approximately 4%-9% due to seasonal effects. Here the use of a detailed 3D model results in similar or improved accuracy over site survey methods, indicating that, with proper description of shade obstructions, modelling of partially shaded PV arrays can be done completely remotely, potentially saving time and cost.

14 SOLAR ENERGY↗
DOE OSTI2022

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↗
DOE OSTI2021

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↗
DOE OSTI2021

Real time operation of high-capacity electric vehicle ridesharing fleets

We study the feasibility of using electric vehicles in online, high-capacity ridepooling systems. Prior work has shown that online algorithms perform well for centrally-controlled, high-capacity ridepool systems. First, we propose a mixed integer linear program to expand past algorithms on ridepooling to electric vehicle fleets with the objective of scheduling vehicle charging to maintain sufficient fleet sizes at various times of day. Then we show a faster, scalable algorithm with similar performance that is practical for full-scale systems. Furthermore, our contributions reinforce the importance of having knowledge and estimates of future demand even when operating in the online setting.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2022

Wash Vehicle Fleet Sizing for Contingency Planning Against Dust Storms

Wash vehicles containing either high- or low-pressure water sprayers, a collection of rotating brushes, or a combination of these, are frequently utilized in concentrating solar power (CSP) plants to maintain a high level of optical efficiency in the solar field. In recent years, multiple modeling approaches have been developed to obtain fleet sizes and mirror-washing schedules that optimize the tradeoff of vehicle capital and use costs and labor versus lost revenues due to soiling. These planning models cover normal operating conditions well but do not consider rare events such as dust storms which can cause a significant reduction in receiver productivity, or shut down operations until most or all of the solar field's mirrors have been cleaned. To that end, we propose a methodology that evaluates whether additional capital should be deployed to hedge against these events by weighing the net present value of the expected benefits against the capital costs. The output of this method is a breakeven frequency, a metric we sue to determine whether an additional vehicle should be purchased to address the contingency of dust storms by comparing it to the expected annual storm frequency We develop a small collection of case studies using commercial-scale CSP tower plants and obtain breakeven frequencies that mostly fall between 0.1 and 1.0 storms per year, depending on the existing fleet size and storm severity.

concentrating solar power↗
DOE OSTI2024

Optimal Managed Fast-Charging Model for Electric Vehicle Fleets with High Utilization and Multiple Charge-Acceptance Curves

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet - under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Fort Vancouver National Historic Site

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for the U.S. Department of Energy’s Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (ITSNA) to collect data on federal fleet operations as part of the Advanced Vehicle Testing Activity’s Federal Fleet Vehicle Data Logging and Characterization study. The Advanced Vehicle Testing Activity study seeks to collect data to validate the use of advanced electric drive vehicle transportation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for NASA White Sands Test Facility

This report focuses on the NASA White Sands Test Facility (WSTF) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (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 (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Sleeping Bear Dunes National Lakeshore

This report focuses on the Sleeping Bear Dunes National Lakeshore (SLBE) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (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 (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗