2013 VTO Annual Merit Review Presentations Analysis [Slides]
Abstract not provided.
33 ADVANCED PROPULSION SYSTEMS↗
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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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Individual Motorist Data - Ohio EV Ownership Trends
The individual motorist dataset contains data and analysis of consumer electric vehicle (EV) ownership trends in rural Appalachian Ohio in comparison with statewide trends. The data span four years, from Q1 2020 to Q2 2023 (partial). They are sourced from the Ohio Bureau of Motor Vehicles registration records and contain detail on drivetrain type (battery-electric vehicle [BEV] or plug-in hybrid electric vehicle [PHEV]); specific vehicle make and model; and registration location at county, city, and ZIP code levels of spatial resolution. Registration data are analyzed at the county level against such indicators as median income, poverty status, urban-rural status, and density of public charging infrastructure. In addition to tabular data, a GIS shapefile with many analysis fields joined is included.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
High-Fidelity Heavy-Duty Vehicle Modeling Using Sparse Telematics Data
Heavy-duty commercial vehicles consume a significant amount of energy due to their large size and mass, directly leading to vehicle operators prioritizing energy efficiency to reduce operational costs and comply with environmental regulations. One tool that can be used for the evaluation of energy efficiency in heavy-duty vehicles is the evaluation of energy efficiency using vehicle modeling and simulation. Simulation provides a path for energy efficiency improvement by allowing rapid experimentation of different vehicle characteristics on fuel consumption without the need for costly physical prototyping. The research presented in this paper focuses on using real-world, sparsely sampled telematics data from a large fleet of heavy-duty vehicles to create high-fidelity models for simulation. Samples in the telematics dataset are collected sporadically, resulting in sparse data with an infrequent and irregular sampling rate. Captured in the dataset was geospatial information, time series measurements, and vehicle-specific metadata from a subset of 96 vehicles from varied geographic regions across North America. A series of custom algorithms was developed to process vehicle data and derive both vehicle model input parameters and representative drive cycles. Derived models provide a basis on which to simulate real-world vehicles and iterate on vehicle aerodynamics, auxiliary power loads, transmission shift schedules, and other parameters to achieve reduced fuel consumption and increase energy efficiency. Notably, these models were developed without the use of expensive field data collection, using only data collected through fleet telematics. Processed representative drive cycles are used to validate the fuel economy of derived models. The models developed through this research allow for more representative vehicle simulations with increased flexibility regarding vehicle-to-vehicle variations.
ADVANCED PROPULSION SYSTEMS↗
Systems integration of lunar Campsite vehicles
This paper describes the configuration design and subsystems integration resolution for lunar Campsite vehicles and the crew vehicles (CVs) which support them. This concept allows early return to the moon while minimizing hardware development. Once in place, the Campsite can be revisited for extended periods. Configuration and operations issues are addressed, and explanations of the parametric subsystem analysis, as well as descriptions of the hardware concept and performance data, are provided. Within an assumed set of launch and mission constraints, a common vehicle stage design for both the Campsite and the CV landers was the chief design driver. Accommodation of a heat-shielded, ballistic crew transportation/return vehicle, scars for later system growth and upgrades, landing the crew in close proximity to the Campsite, and appropriate kinds of robotic systems were all secondary design drivers. Physical integration of the crew module and airlock, structural system, thermal radiators, power production and storage systems, external life support consumables, and payloads are covered. The vehicle performance data were derived using a Boeing lunar transportation sizing code to optimize vehicle stage sizes and commonality. Configuration trades were conducted and detailed sketches were produced.
Capps, Stephen↗
Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Analysis
This Technical Publication (TP) is meant to address a number of topics related to the application of Monte Carlo simulation to launch vehicle design and requirements analysis. Although the focus is on a launch vehicle application, the methods may be applied to other complex systems as well. The TP is organized so that all the important topics are covered in the main text, and detailed derivations are in the appendices. The TP first introduces Monte Carlo simulation and the major topics to be discussed, including discussion of the input distributions for Monte Carlo runs, testing the simulation, how many runs are necessary for verification of requirements, what to do if results are desired for events that happen only rarely, and postprocessing, including analyzing any failed runs, examples of useful output products, and statistical information for generating desired results from the output data. Topics in the appendices include some tables for requirements verification, derivation of the number of runs required and generation of output probabilistic data with consumer risk included, derivation of launch vehicle models to include possible variations of assembled vehicles, minimization of a consumable to achieve a two-dimensional statistical result, recontact probability during staging, ensuring duplicated Monte Carlo random variations, and importance sampling.
Hanson, J. M.↗
DRF: A Software Architecture for a Data Marketplace to Support Advanced Air Mobility
Advanced Air Mobility is a new aviation vision, where unmanned aerial systems will trans- port passengers and cargo across urban and rural areas. Critical to the realization of this vision is the development of a digital marketplace, which allows service providers and consumers operating in the airspace ecosystem to securely exchange data and reasoning insights. In this paper, we present the architecture of a decentralized data marketplace that connects data and reasoning service providers to vehicles and other service consumers along the cloud-to-edge continuum. We also present two example use cases to demonstrate the value of our approach.
Autonomy↗
Transportation vehicle energy intensities. A joint DOT/NASA reference paper
A compilation of data on the energy consumption of air and ground vehicles is presented. The ratio BTU/ASM, British Thermal Units/Available Seat Mile, is used to express vehicle energy intensiveness, and related to the energy consumed directly in producing seat-mile or ton-mile productivity. Data is presented on passenger and freight vehicles which are in current use or which are about to enter service, and advanced vehicles which may be operational in the 1980's and beyond. For the advanced vehicles, an estimate is given of the date of initial operational service, and the performance characteristics. Other key considerations in interpreting energy intensiveness for a given mode are discussed, such as: load factors, operations, overhead energy consumption, and energy investments in new structure and equipment.
Mascy, A. C.↗
Data Quality Assessment of Optiwatt Vehicle Telematics Data
In October 2024, the Idaho National Laboratory (INL) received data from Optiwatt (Compass Global, Inc.) describing the driving and charging behavior of electric vehicle (EV) drivers. The data shared had been collected from approximately 10,000 vehicles and included vehicle specifications, driving information like odometer readings at the beginning and end of origin-destination pairs (i.e., trips with identification of home for trip start and end for Tesla vehicles), and charging information such as charging energy consumed per charge session and if the charge occurred at home. The vehicle data were provided from 9 EV makes and 18 EV models, with production years ranging from 2012–2024, but more than 9,500 of the vehicles were Tesla EVs. The data includes more than six million trips and more than three million charging events that occurred between June 2023 to Aug 2024 and collected from California and the Eastern United States. The purpose of this report is to review the quality of the data received from Optiwatt and the feedback INL received from Optiwatt after data concerns were shared with them.
33 - ADVANCED PROPULSION SYSTEMS↗
Transportation Energy: A Comparison Including and Excluding Upstream Energy
Thirty-eight editions of the Transportation Energy Data Book (Data Book) have been published since the report began in 1976. It was designed as a desk-top reference of statistics and information that characterize transportation activity. The most recent Data Book, edition 38, has 226 tables and 70 figures in the body of the report, with an additional 51 tables in appendices. One of the most-often used tables in the Data Book is referred to by the authors as “the big energy table.” It has transportation energy use in British thermal units (Btu) by mode and fuel type. The data are compiled from about 20 different sources and combined to create a detailed picture of transportation energy use. From the first edition to edition 35 of the Data Book, electricity use in the big energy table was converted from the original kilowatt-hours (kWh) to Btu using the net generation heat rate for fossil fuels. In those days, electricity was used mainly by transit rail. As electricity began to be used in the highway modes with consumer sales of electric vehicles, that process was revisited. Since energy use in the Data Book’s big energy table for all other fuels did not include the upstream energy to create the fuel, it seemed inconsistent to include upstream energy for generation of electricity. Beginning with edition 36, electricity use for the big energy table was converted from kilowatt hours to Btu using the heat content of electricity. Using heat content instead of net generation reduced electricity use data by about two-thirds. Feedback in recent years on the change in process for electricity led to an investigation of the differences in transportation energy use when adding estimates of upstream energy use to all the fuels used in transportation. This report documents the result of that effort.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Customer Experience at Public Charging Stations and Its Effects on the Purchase and Use of Electric Vehicles
To support the ChargeX Consortium’s mission to measure and significantly improve the reliability and usability of public charging infrastructure in North America, researchers in the consortium from the University of Washington (UW) reviewed the published literature and data from consortium members to evaluate how the consumer experience at public fast charging stations influences electric vehicle (EV) adoption.
33 ADVANCED PROPULSION SYSTEMS↗
Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation
Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.
Civil engineering↗
Approximation Framework of Embodied Energy of Safety: Insights and Analysis
Transportation safety, as a critical component of an efficient and reliable transportation system, has been extensively studied with respect to societal economic impacts by transportation agencies and policy officials. However, the embodied energy impact of safety, other than induced congestion, is lacking in studies. This research proposes an energy equivalence of safety (EES) framework to provide a holistic view of the long-term energy and fuel consequences of motor vehicle crashes, incorporating both induced congestion and impacts from lost human productivity resulting from injury and fatal accidents and the energy content resulting from all consequences and activities from a crash. The method utilizes a ratio of gross domestic product (GDP) to national energy consumed in a framework that bridges the gap between safety and energy, leveraging extensive studies of the economic impact of motor vehicle crashes. The energy costs per fatal, injury, and property-damage-only (PDO) crashes in gasoline gallon equivalent (GGE) in 2017 were found to be 200,259, 4442, and 439, respectively, which are significantly greater than impacts from induced congestion alone. The results from the motor vehicle crash data show a decreasing trend of EES per crash type from 2010 and 2017, due primarily in part to a decreasing ratio of total energy consumed to GDP over those years. In addition to the temporal analysis, we conducted a spatial analysis addressing national-, state-, and local-level EES comparisons by using the proposed framework, illustrating its applicability.
99 GENERAL AND MISCELLANEOUS↗
Spatially Resolved Domicile Charging Demands for Light-, Medium-, and Heavy-Duty Electric Vehicles in Virginia
The use of plug-in electric vehicles (PEVs) and resulting grid impacts are likely to grow rapidly, and evaluation of optimal smart charge management and grid integration strategies is warranted now. Evaluating distribution grid impacts requires fine-grained models of PEV operations to estimate charging loads across diverse vehicles at high spatial resolution. We propose such a model and consider a high-electrification scenario in Richmond and Newport News, Virginia. Our framework considers four categories of vehicle that are amenable to early aggressive electrification: light-duty passenger vehicles (LDV), trucks and vans with a focus on delivery or other local operations, school buses, and transit buses. These vehicles have a relatively consistent domicile, reducing the need for public charging infrastructure rollout to electrify. We apply a recent LDV model and propose new models for each vocation of medium- and heavy-duty vehicle, leveraging telematics data. We demonstrate our framework in Virginia and find energy demands in the region may total 15 GWh day, with most consumed by LDV. However, considering power demand at high spatial resolution reveals a different trend: LDVs have relatively small peak loads at specific sites (peak site demand around 800 kW) compared to average and high demand medium- and heavy-duty vehicle charging sites (peak site demand around 6,000 kW at a transit bus depot, 1,500 kW at a local freight hub, and 1,000 kW at a school). Our framework yields insights on the relative impacts of each vocation and enables future work to tailor grid integration strategies to each vehicle category.
33 ADVANCED PROPULSION SYSTEMS↗
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.
Shaw Cortez, Wenceslao E.↗
OT Operational Anomaly Detection (OAD) T&D + DER
The growth of utility-scale renewable energy resources, distributed energy resources (DER), and transportation electrification has increased uncertainty and cybersecurity risks in power grids. The Purdue Enterprise Reference Architecture model which is widely adopted by the utility industry is now insufficient to protect the power grid against cyber-attacks. There is a need to identify what cybersecurity model is effective on Energy Management System (EMS), Advanced Distribution Management System (ADMS), and DER Management System (DERMS) to address the fundamental cybersecurity challenges in the age of increasing renewable energy and DER share as well as consumer participation in the electric energy industry. The next generation of cybersecurity model for OT network should be able to detect inside attackers, mitigate the cybersecurity risks arising from the new grid participants including DER aggregators, electric vehicle owners, and behind-the-meter consumers outside the utility company, and develop the strategy to trust consumer measurement data. This panel will discuss the challenges and pathways for the development of an ensemble cybersecurity model based on predictive state estimation to detect cybersecurity anomalies in OT network including EMS, ADMS, and DERMS.
cybersecurity↗
Holistic Transportation and Energy Modeling
This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers
Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.
36 MATERIALS SCIENCE↗
2019 California Vehicle Survey
The 2019 California Vehicle Survey of residential and commercial light-duty fleet owners in California assessed consumer preferences for vehicles and included a targeted sample of plug-in electric vehicle (PEV) owners. In addition to economic and demographic data, the survey integrated light-duty vehicle holding and use information with vehicle choice data, which was collected via a set of eight exercises on vehicle and fuel type choice. The PEV owner survey participants provided additional data on charging behavior, electricity rates, and their main motivations for purchasing PEVs.
1Hz data↗