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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario

This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Shifts in pore connectivity from precipitation versus groundwater rewetting increases soil carbon loss after drought

Droughts and other extreme precipitation events are predicted to increase in intensity, duration and extent, with uncertain implications for terrestrial carbon (C) sequestration. Soil wetting from above (precipitation) results in a characteristically different pattern of pore-filling than wetting from below (groundwater), with larger, well-connected pores filling before finer pore spaces, unlike groundwater rise in which capillary forces saturate the finest pores first. Here we demonstrate that pore-scale wetting patterns interact with antecedent soil moisture conditions to alter pore-, core- and field-scale C dynamics. Drought legacy and wetting direction are perhaps more important determinants of short-term C mineralization than current soil moisture content in these soils. Our results highlight that microbial access to C is not solely limited by physical protection, but also by drought or wetting-induced shifts in hydrologic connectivity. We argue that models should treat soil moisture within a three-dimensional framework emphasizing hydrologic conduits for C and resource diffusion.

Carbon↗
DOE OSTI2024

Exchange–correlation entropy from the generalized thermal adiabatic connection

Warm dense matter is a highly energetic phase characterized by strong correlations, thermal effects, and quantum mechanical electrons. Thermal density functional theory is commonly used in simulations of this challenging phase, driving the development of temperature-dependent approximations to the exchange–correlation free energy. Approaches using the adiabatic connection formula are well known at zero temperature and have been recently leveraged at non-zero temperatures as well. In this work, a generalized thermal adiabatic connection (GTAC) formula is proposed, introducing a fictitious temperature parameter. This allows extraction of the exchange–correlation entropy S XC using simulated interaction strength scaling. Here, this procedure uses a Hellmann–Feynman approach to express the exchange–correlation entropy in terms of a temperature- and interaction strength-dependent exchange–correlation potential energy. In addition, analysis of S XC as a function of interaction strength suggests new forms for approximations, and GTAC itself offers a new framework for exploring both the exact and approximate interplay of temperature, density, and interaction strength across a wide range of conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Time–connectivity superposition and the gel/glass duality of weak colloidal gels

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Reduced-Order Structure-Preserving Model for Parallel-Connected Three-Phase Grid-Tied Inverters

Next-generation power networks will contain large numbers of grid-connected inverters satisfying a significant fraction of system load. Since each inverter model has a relatively large number of dynamic states, it is impractical to analyze complex system models where the full dynamics of each inverter are retained. To address this challenge, we derive a reduced-order structure-preserving model for parallel-connected grid-tied three-phase inverters. Here, each inverter in the system is assumed to have a full-bridge topology, LCL filter at the point of common coupling, and the control architecture for each inverter includes a current controller, a power controller, and a phase-locked loop for grid synchronization. We outline a structure-preserving reduced-order inverter model with lumped parameters for the setting where the parallel inverters are each designed such that the filter components and controller gains scale linearly with the power rating. By structure preserving, we mean that the reduced-order three-phase inverter model is also composed of an LCL filter, a power controller, current controller, and PLL. We show that the system of parallel inverters can be modeled exactly as one aggregated inverter unit and this equivalent model has the same number of dynamical states as any individual inverter in the system. Numerical simulations validate the reduced-order model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Nonlinear Direct Power Controller for a Three-Phase Grid-Connected Inverter with Online Parameter Update for PV Application

This work proposes a nonlinear direct power controller for a three-phase grid-connected inverter for photovoltaic application. Grid-connected converters for solar applications require advanced control architectures to ensure optimal operation and extraction of maximum power from the photovoltaic arrays. Therefore, to ensure this, a nonlinear power controller architecture has been proposed to extract the maximum power from the solar arrays and to achieve the most optimal operation. To further improve the performance, a parameter update scheme has been utilized which is based on model reference adaptive system. The latter helps to accomplish not only good dynamic response but also excellent tracking performance. The overall control come estimation architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

direct power control↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2019

Impedance-Based Prediction of Distortions Generated by Resonance in Grid-Connected Converters

Small-signal impedance-based analysis can effectively predict the frequency and damping of resonance modes in power electronic systems. However, it cannot predict the magnitude of resonance or explain resonance-generated distortions in the absence of an external disturbance at the resonance frequency. This paper presents an impedance-based theory for the prediction of the magnitude of resonance or resonance-generated distortions in grid-connected converters. It is discovered that the impedance response of a converter starts changing with the magnitude of resonance at its terminals. The changing converter impedance response may stabilize an unstable growing resonance mode beyond a certain magnitude, at which point the converter enters a limit cycle mode of sustained oscillations. The proposed theory uses large-signal impedance for the prediction of resonance-generated distortions; the large-signal impedance of an electrical equipment represents its impedance response for different magnitudes of perturbation injected at its terminals. Large-signal impedance-based prediction of resonance-generated distortions is demonstrated for a three-phase grid-connected voltage-source converter.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

The Effect of the Gate-Connected Field Plate on Single-Event Transients in AlGaN/GaN Schottky-Gate HEMTs

A focused pulsed X-ray beam is used to determine how the redistribution of the electric field by the gate-connected field plate affects single-event transient (SET) susceptibility of an AlGaN/GaN Schottky-gate HEMT on SiC. SETs generated by scanning the X-ray beam across the HEMT depend strongly on the presence of the field plate, radiation strike location, bias conditions, and X-ray photon energy. Furthermore, for the particular HEMT we tested, the gate-connected field plate reduces the electric-field strength near the edge of the gate by a factor of approximately 2, which results in faster decaying transients and less collected charge.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Lifelong population connectivity between large rivers and their tributaries: A case study of shovelnose sturgeon from the Mississippi and Wisconsin rivers

Fishes that depend on large–river habitats have declined in many regions due to human alteration of both mainstems and tributaries. Conserving these species is often complicated by their dependence on long–distance movements to complete their life cycle, but the location of key habitats is difficult to assess across multiple life history stages. Movement of large–river fishes between mainstems and major tributaries is commonly observed, but the role of these exchanges at the population level is uncertain. We used shovelnose sturgeon ( Scaphirhynchus platorynchus ) as a case study for assessing the lifelong spatial ecology of large–river fishes. Fin ray microchemistry data indicate that shovelnose sturgeon from the Mississippi and Wisconsin rivers represent a unified population that relies on connections among multiple large–river habitats throughout life history. The tributary serves as an important spawning area, but fin ray chemistry data suggest larvae subsequently drift downstream to nursery habitats in the mainstem; an assertion that is supported by extensive field survey data. Our analyses underscore the lifelong importance of connections within river networks for maintaining viable populations of this, and likely other large–river species that have been documented to use tributary habitats. Furthermore, these findings support a metapopulation perspective on species that depend on large–river habitats, many of which are now highly altered and fragmented. For shovelnose sturgeon and species with similar life history, protecting or restoring linkages between mainstem rivers and their tributaries should be a conservation priority.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗
DOE OSTI2018

Connectivity and functional verification for the LSST science raft towers

The LSST Camera focal plane will be constructed with 21 144-Mpixel modules (\Raft Tower Module", RTM). An extensive operational test is performed to confirm the integrity of all connections and to verify the basic functionality. Each RTM undergoes at least four connectivity tests. A python script communicates with Java- based control software and performs the test. A python script parses the test data and generates a PDF report. The report includes a summary PASS/FAIL table, several hundred current, voltage and temperature parameters, and images taken with the CCD array at room temperature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

NREL Topic 1 Final Report: Cohesive Application of Standards-Based Connected Devices to Enable Clean Energy Technologies

This document is the final report of a two-year development, test, and demonstration project entitled 'Cohesive Application of Standards-Based Connected Devices to Enable Clean Energy Technologies.' The project was part of the National Renewable Energy Laboratory's (NREL) Integrated Network Test-bed for Energy Grid Research and Technology (INTEGRATE) initiative. The Electric Power Research Institute (EPRI) and a team of partners were selected by NREL to carry out a project to develop and test how smart, connected consumer devices can act to enable the use of more clean energy technologies on the electric power grid. The project team includes a set of leading companies that produce key products in relation to achieving this vision: thermostats, water heaters, pool pumps, solar inverters, electric vehicle supply equipment, and battery storage systems. A key requirement of the project was open access at the device level - a feature seen as foundational to achieving a future of widespread distributed generation and storage. The internal intelligence, standard functionality and communication interfaces utilized in this project result in the ability to integrate devices at any level, to work collectively at the level of the home/business, microgrid, community, distribution circuit or other. Collectively, the set of products serve as a platform on which a wide range of control strategies may be developed and deployed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Zero-Emission Bus Evaluation Results: County Connection Battery Electric Buses

The U.S. Department of Transportation's (DOT's) Federal Transit Administration (FTA) supports the research, development, and demonstration of low- and zero-emission technology for transit buses. FTA funds research projects with a goal of facilitating commercialization of advanced technologies for transit buses that will increase efficiency and improve transit operations. DOT's Research, Development, and Technology Office (OST-R) also has an interest in zero-emission bus (ZEB) technology deployment and commercialization. OST-R is coordinating and collaborating with FTA on the evaluation process and results by providing funding to cover additional evaluations. FTA and OST-R are collaborating with the U.S. Department of Energy (DOE) and DOE's National Renewable Energy Laboratory (NREL) to conduct in-service evaluations of advanced technology buses developed under its programs. NREL uses a standard evaluation protocol for evaluating the advanced technologies deployed under the FTA programs. FTA seeks to provide results from new technologies being adopted by transit agencies. The eight evaluations selected to date include battery electric buses (BEBs) and fuel cell electric buses (FCEBs) from different manufacturers operating in fleets located in both cold and hot climates. The purpose of this report is to present the results from Central Contra Costa Transit Authority (County Connection) deployment of four BEBs in Concord, California. NREL's evaluation of the BEBs at County Connection was funded by OST-R.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

An Authentication Vulnerability Assessment of Connected Lighting Systems

Emerging connected lighting systems (CLS) that incorporate distributed intelligence, network interfaces, and sensors can become data-collection platforms that enable a wide range of valuable new capabilities as well as greater energy savings in buildings and cities. However, CLS technology is currently at an early stage of development, and its increased connectivity introduces cybersecurity risks that are new to the lighting industry and that must be addressed for successful integration with other systems. While a number of existing frameworks, guidelines, and tests for evaluating cybersecurity vulnerability may apply to CLS in whole or in part, there is currently no mandatory requirement for cybersecurity testing or certification. The lighting industry, including technology developers and specification organizations, is currently evaluating the suitability of existing frameworks and guidelines for CLS. To support these efforts, Pacific Northwest National Laboratory (PNNL) is conducting a series of studies intended to educate lighting industry stakeholders on specific cybersecurity practices and characterize their implementation in commercially available CLS with varying system architectures, network-communication technologies, and degrees of maturity. This study demonstrates that tests for authentication vulnerabilities can be developed with objective pass/fail criteria, therby facilitating comparisons between CLS. Based on the limited results of this study, it appears that the CLS that are being brought to market have varying levels of authentication vulnerability. It is hoped that these evaluations will support and perhaps accelerate industry discussions on the risks of specific security vulnerabilities, what vulnerabilities should be addressed by in-development of future lighting-specific best practices, and whether any such practices should be included in voluntary lighting standards.

42 ENGINEERING↗
DOE OSTI2025

Larval connectivity for European green crab management in the Salish Sea and surrounding waters

The presence of invasive species is a growing concern in coastal marine ecosystems because of their adverse effects on biodiversity. The European green crab Carcinus maenas (EGC) is a small crab inhabiting inshore areas. Although it is native to the Northeast Atlantic Ocean and Baltic Sea, its distribution has expanded to North America, where it is an invasive species. Its main food sources are small invertebrate species that support valuable fisheries in the USA. The first presence of EGC in northern Washington was observed around 20 yr ago, along the Pacific Coast of the USA. Recently, EGC has been detected throughout the Salish Sea (Washington, USA, and British Columbia, Canada) wherein spread dynamics are unknown. The overall distribution of EGC is mainly driven by larval dispersal and, in the Salish Sea and surrounding waters, the assessment of EGC population dynamics is essential to understand its migration patterns and prevent its future expansion. To investigate the dispersal patterns of EGC larvae, a larval dispersal model was developed which couples a regional model of hydrodynamic circulation with an individual-based model of ichthyoplankton dynamics. Simulations were performed over 9 yr (2013-2022) to analyze average larval transport trends in the Salish Sea and surrounding waters, interannual variability of EGC larval connectivity, and the influence of larval behavior on connectivity patterns. Lastly, areas were identified to inform invasive species management moving forward. The prediction of likely sources and settlement locations of EGC larvae from the model will help improve the management of the population in the Salish Sea and surrounding waters.

60 APPLIED LIFE SCIENCES↗