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DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Improving commercial truck fleet composition in emission modeling using 2021 US VIUS data

Commercial trucks are essential elements of the nation's supply chain system. Meanwhile, intensive truck movements contribute significantly to system externalities, such as energy use and air pollution. However, collecting detailed fleet composition and distribution of operational patterns remains a barrier to accurately accounting for these impacts. The recently released 2021 US Vehicle Inventory and Use Survey (US VIUS) fills a critical gap in understanding commercial truck fleet distributions, their operations, and business constraints at the national scale. This study aims to understand the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and calibrate the fleet inputs in regulatory emission models to assess the potential emission implications of the VIUS-derived fleet composition. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA's MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to improve fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The study also investigates potential emission reduction benefits under various forecasted fleet electrification scenarios. The energy consumption and critical air pollutant rates by vehicle types are compared between MOVES4 and US VIUS fleets for both current and future scenarios to provide insights into the latest U.S. commercial vehicle fleet characteristics and their implications on energy and emissions. This study helps policymakers and practitioners advance the commercial fleet generation for emission models. It also deepens the understanding of the emission reduction potential of the commercial fleet under various fleet projections.

2021 US VIUS↗

DOE OSTI2025

Commercial Fleet Level Emissions and Energy Tracker (COFLEET) v1.0

This tool generates the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and assess the fleetwide energy and emission outcomes. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA's MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to generate fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The tool also provides fleet turnover and emission forecasts under various forecasted fleet electrification scenarios. This tool helps policymakers and practitioners advance the commercial fleet generation for emission models. This study also provides some sample datasets for state and local transportation/air quality agencies to test, which can reduce the estimation bias associated with using MOVES default fleets.

Xu, Xiaodan [Lawrence Berkeley National Laboratory↗

DOE OSTI2022

HIVE™ [SWR-19-36]

The HIVE™ platform is a mobility services simulation platform developed to provide insight on the energy, infrastructure, service, and economic outcomes of various mobility as a service (MaaS) options. The HIVE platform takes a set of spatiotemporal travel origin-destination pairs and simulates the operation of a predefined mobility service fleet, incorporating request pooling, and various operational and charging behaviors. Hive specializes at modeling fleets of automated electric vehicles (AEVs) and can be used to site and size direct current fast charge (DCFC) stations and measure grid impacts of large-scale AEV fleets serving real-world MaaS trip demand (similar to taxis, Uber, Lyft, etc.). Potential outcomes from a Hive simulation include level of service, total vehicle miles traveled (VMT), deadheading (zero passenger) miles, simultaneous and total energy loads, average occupancy, and more. Hive is developed to generalize to new regions and can be customized to handle many scenarios and operating conditions.

Rames, Clement↗

DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗

NASA NTRSDate not supplied

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding, which used a blended coding approach to generate actionable heuristics that stakeholders (e.g., researchers, UI designers, software developers) could use to answer specific research questions and make future design decisions. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

high density vertiplex↗

NASA NTRSDate not supplied

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

high density veriplex↗

NASA NTRS1975

Tug ground operations - An approach to early identification of operational impacts

An approach to early identification of operational requirements is discussed together with an assessment of the impact of these requirements on the development and acquisition phase of a program. The approach is applied to the Space Tug program. Some of the more salient results of the Tug Fleet and Ground Operations Schedules and Controls study performed under a NASA/Marshall Space Flight Center contract are provided. Ground-processing requirements and flows are presented, including a summary of facility requirements, timelines, activities and options/contingency-provision recommendations. Programmatic recommendations resulting from the study are presented in areas of payload integration, activation requirements, fleet utilization, fleet sizing, costs, and WTR delta requirement for the Tug.

Best, J. L.↗

DOE OSTI2022

Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Influence of operation management on fuel consumption of coach fleet

Not Available

Ji, Shaobo↗

DOE OSTI2021

Performance Index Assessment for the PV Fleet Performance Data Initiative

We report on 250 PV systems throughout the United States, comprising 157 MWdc of system capacity and more than 10,000 monthly performance index (PI) values. Loss factors were isolated including first-year start-up issues, snowfall, soiling and inverter downtime. Inverter availability was found to contribute significant system energy loss during the first six months of operation, with an average of 8% loss occurring during this period, and 2.3% on average thereafter. Other start-up issues beyond inverter downtime, such as partial string outage, contributed additional underperformance in the first year of operation across the fleet. Winter performance was also found to be below summer performance on average, likely due to snowfall. A relationship was found between monthly snowfall accumulation in centimeters and monthly under-performance, indicating a 6%-40% loss in months with measured snowfall, depending on climate. After correcting for availability, snow and startup loss, over 90% of systems were performing within 10% of monthly expectation based on satellite resource data and PVWatts production estimates.

loss factors↗

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 OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

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↗

NASA NTRS2010

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods

Das, Santanu↗

DOE OSTI2021

Microgrid Fast Charging Station (MFCS) Design Platform Project (Summary Report)

This report documents the important steps and outcomes of the Microgrid Fast Charging Station (MFCS) Design Platform project, executed by XENDEE Corporation and its testing and validation by Idaho National Laboratory (INL). This final summary report builds on the first summary released on April 30th, 2021 and adds the final results from the Hardware in the Loop (HIL) simulations and tests, which have been completed July 14th 2021. With this report XENDEE Corporation and Idaho National Laboratory conclude the first version of the Microgrid Fast Charging Station (MFCS) Design Platform as well as all related tests and validations for two in-depth case studies for islanded and non-islanded operation. The platform itself utilizes XENDEE’s advanced modeling systems and INL’s HIL system to ensure project viability and technical feasibility. It also intelligently maps all cables, transformers, and distributed technology interactions to anticipate and mitigate problems during peak usage or adverse conditions. Finally, the system is designed to optimize dispatch and generation at each time step of the day allowing the Microgrid to take advantage of energy sales to the utility and best manage the charging of an electrical fleet. This allows operators to reliably build bankable Microgrid systems and to operate them to reach the maximum efficiency even under the dynamic needs of electric vehicle fast charging

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2017

Model-Driven Development of Safety Architectures

We describe the use of model-driven development for safety assurance of a pioneering NASA flight operation involving a fleet of small unmanned aircraft systems (sUAS) flying beyond visual line of sight. The central idea is to develop a safety architecture that provides the basis for risk assessment and visualization within a safety case, the formal justification of acceptable safety required by the aviation regulatory authority. A safety architecture is composed from a collection of bow tie diagrams (BTDs), a practical approach to manage safety risk by linking the identified hazards to the appropriate mitigation measures. The safety justification for a given unmanned aircraft system (UAS) operation can have many related BTDs. In practice, however, each BTD is independently developed, which poses challenges with respect to incremental development, maintaining consistency across different safety artifacts when changes occur, and in extracting and presenting stakeholder specific information relevant for decision making. We show how a safety architecture reconciles the various BTDs of a system, and, collectively, provide an overarching picture of system safety, by considering them as views of a unified model. We also show how it enables model-driven development of BTDs, replete with validations, transformations, and a range of views. Our approach, which we have implemented in our toolset, AdvoCATE, is illustrated with a running example drawn from a real UAS safety case. The models and some of the innovations described here were instrumental in successfully obtaining regulatory flight approval.

Safety case↗