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DOE OSTI2024

A Large-Scale Analysis to Optimize the Control and V2V Communication Protocols for CDA Agreement-Seeking Cooperation

Cooperative driving automation (CDA) Class C, agreement-seeking cooperation, is an innovative and practical solution that can promote cooperation among general passenger vehicles on the road. However, more comprehensive studies are needed before establishing the standard protocols of agreement-seeking cooperation, such as communication frequency and the duration of cooperation. Here, this article presents an initiative study on the impacts of communication capabilities on agreement-seeking cooperation. Through a large-scale analysis by regulating vehicle-to-vehicle (V2V) communication metrics, this work suggests desirable system parameters that can maximize the benefits of cooperation and ensure reliable operability while avoiding exhaustive communication loads. As the first step, an example agreement-seeking cooperation system is created for a car-following scenario, including decision-making and control algorithms for autonomous vehicles. Then, software-in-the-loop tests explore the performance of the developed system as it encounters various communication risks, such as latency and message packet drops. The system performance metrics are evaluated from various angles, including the time consumed for the agreement-seeking process, cooperation ratio, and the ratio of faulty cooperation. Energy saving from the cooperation is assessed by using simulation software that can run multiple high-fidelity vehicle models simultaneously. Based on the analyses, this article suggests the V2V communication requirements for the reliable operation of CDA agreement-seeking, which can be referred to when developing the standard protocols of agreement-seeking cooperation.

42 ENGINEERING↗

DOE OSTI2019

PV Cyber Security Research (Final Report)

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cyber security attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid support functions which can drastically influence power quality, voltage, and bulk system frequency. This project was split into two phases. The first provided a survey and roadmap of the cybersecurity for the solar industry. The second investigated multiple PV cybersecurity research and development (R&D) concepts identified in the first phase. In the first year, the team created a roadmap for improving cybersecurity for distributed solar energy resources. This roadmap was intended to provide direction for the nation over the next five years and focused on the intersection of industry and government and recommends activities in four related areas: stakeholder engagement, cyber security research and development, standards development, and industry best practices. At the same time, the team produced a primer for DER vendors, aggregators, and grid operators to establish a common taxonomy and describe basic principles of cyber security, encryption, communication protocols, DER cyber security recommendations and requirements, and device-, aggregator-, and utility-level security best practices to ensure data confidentiality, integrity, and availability. This material was motivated by the need to assist the broader PV industry with cybersecurity resilience and describe the state-of-the-art for securing DER communications. Lastly, an adversary-based assessment of multiple PV devices was completed at the Distributed Energy Technologies Laboratory at Sandia National Laboratories to determine the status of industry cybersecurity practices. The team found multiple deficiencies in the security features of the assessed devices. In the second year, a set of recommendations was created for DER communication protocols— especially with respect to the state-of-the-art requirements in IEEE 2030.5. Additionally, several cybersecurity R&D technologies related to communications-enabled photovoltaic systems were studied to harden DER communication networks. Specifically, the team investigated (a) using software defined networking to create a moving target defense system for DER communications, and (b) engineering controls that prevent misprogramming or adversary action on DER devices/networks by disallowing setpoints that will generate unstable power system operations.

14 SOLAR ENERGY↗

DOE OSTI2023

First principles optimization of plutonium electrorefining

Herein this work presents a means of controlling plutonium electrorefining at a maximum rate regardless of equipment setup through the derivation of power supply current and potential governing equations for normal and off-normal operations. The governing equations are demonstrated by electrorefining surrogate materials. A simple linear current sweeping method was used to determine the maximum electrorefining current for the surrogate system. This method can be used to develop autonomous process optimization, real-time online processing monitoring, and real-time process endpoint detection. Ultimately, this research provides the foundation to optimize the liquid metal electrorefining rate to decrease the time needed to the physical limit for the process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots

Defining quantum dots in semiconductor-based heterostructures is an essential step in initializing solid-state qubits. With growing device complexity and increasing number of functional devices required for measurements, a manual approach to finding suitable gate voltages to confine electrons electrostatically is impractical. Here, we implement a two-stage device characterization and dot-tuning process, which first determines whether devices are functional and then attempts to tune the functional devices to the single or double quantum-dot regime. We show that automating well-established manual-tuning procedures and replacing the experimenter’s decisions by supervised machine learning is sufficient to tune double quantum dots in multiple devices without premeasured input or manual intervention. The quality of measurement results and charge states are assessed by four binary classifiers trained with experimental data, reflecting real device behavior. We compare and optimize eight models and different data preprocessing techniques for each of the classifiers to achieve reliable autonomous tuning, an essential step towards scalable quantum systems in quantum-dot-based qubit architectures.

Darulova, J↗

DOE OSTI2022

Data from the Development Evolution of a Vehicle for Custom Control

In order to develop custom controllers intended to operate vehicles on a live highway, a series of data collection-focused tests were performed at increasing stages of complexity. Modern vehicles with features like Adaptive Cruise Control (ACC) feature a rich set of sensors and drive-by-wire mechanisms. The presented stages of data collection begins with the analysis of raw data provided by various vehicles, and eventually results in spoofing Controller Area Network (CAN) protocols for sending control commands to operate a vehicle. This paper covers the data and technical efforts needed at various stages. The raw data and tools to plot the data are also publicly available.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Good Grids Make Good Neighbors

It's great to have neighbors you can depend on, whether you're borrowing a cup of sugar or you need someone to walk your dog while you're out of town. In the western Colorado neighborhood of Basalt Vista, the residents are even closer than most: They share their electricity. But unlike your neighbor with the sugar, the residents of Basalt Vista may not even know when they're being generous. The energy exchanges happen automatically, behind the scenes. What residents do know is how inexpensive, reliable, and renewable their electricity is.

autonomous energy grids↗

DOE OSTI2018

Efficient and Collision-Free Anticipative Cruise Control in Randomly Mixed Strings

Connected intelligent vehicle following can improve safety and efficiency compared to today's road transport, but real traffic in the near future will not provide an ideal setting for its deployment. Unconnected human-driven vehicles will follow variable behavior patterns that combine with differences in dynamic capability to create heterogeneous scenarios. In this paper, connected automated vehicles employ model predictive control for following in traffic that may include both heavy and passenger vehicles at quasi-random positions. Adding further realism, some quasi-randomly mixed vehicles are completely unconnected and equipped with the reactive intelligent driver model using pseudorandom parameters. A mixed-integer quadratic programming formulation adapts the predictive algorithm to diverse powertrain operating point constraints. The preceding vehicle's control input is estimated from velocity and brake light observations and used to probabilistically generate a preview for the ego vehicle. Here, a terminal constraint designed using particle kinematics prevents collisions due to shortsightedness. Results are simulated at various heavy and predictive vehicle concentrations in the presence of packet loss. Linear fuel economy improvements between 1.4% and 1.9% per 10% increase in predictive vehicle penetration rate are shown.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Basic Research Needs Workshop on Synthesis Science for Energy Relevant Technology

The technology that lies at our fingertips becomes more powerful each day. Smartphones connect us instantly to family, friends, and co-workers around the globe; give us access to a limitless stream of information; control the heating in our homes; and serve as our cameras, calculators, flashlights, music players, boarding passes and, on occasion, our phones. Cars are ever more fuel-efficient, safer, semi-autonomous, and have more computing power than the systems that guided humankind to the moon. LED lighting and solar panels are becoming commonplace, replacing less efficient technologies and expanding the energy options available worldwide. Novel polymers and nanoparticles are playing a crucial role in enhanced oil recovery. None of these advances would have been possible without the discovery and development of, and ability to create, new materials and chemical processes. Now imagine what our world would be like if we could accelerate those discoveries a thousandfold. What if the only limit to synthesizing new forms of matter were the imagination? We could build complex assemblies of atoms and molecules with architectures and capabilities far exceeding those of materials found in nature—for example, develop catalysts that turn garbage into fuels, design solar cells to power our homes directly from sunlight, make batteries with the energy density of gasoline, and create one- and two-dimensional solids that transport charge hundreds of times faster than silicon or allow us to build quantum bits based on the spins of electrons or photons to realize the promise of “beyond Moore’s law” computing. Advances in synthesis science are required to bring about this future—we not only must know how to design new molecules and materials with desired functions and properties through theory and computational techniques; we also must be able to make the materials we envision. New approaches to discovering as yet unimagined matter require a sea change in the way we think about the science of synthesis. Chemical and materials sciences have traditionally focused on understanding structure–function relationships with the goal of predicting where the atoms should be placed to achieve a targeted property or process. Much less effort has been directed toward a predictive science of synthesis—understanding how to get the atoms where they need to go to achieve the desired structure. This report, which is the result of the Basic Energy Sciences Workshop on Basic Research Needs for Synthesis Science for Energy Technologies, lays out the scientific challenges and opportunities in synthesis science. The workshop was attended by more than 100 leading national and international scientific experts. Its five topical and two crosscutting panels identified four priority research directions (PRDs) for realizing the vision of predictive, science-directed synthesis.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Smart Methane Emission Detection System Development Final Report

03 NATURAL GAS↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2023

Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) Program (2022 Annual Progress Report)

This document summarizes the progress of VTO's Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) program during the fiscal year 2022. DORMA's R&D focus was on research, development, and demonstration of new propulsion and vehicle technologies that will reduce greenhouse gas emissions in difficult-to-decarbonize transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2013

COMPLIANCE STUDIES: WHAT ABOUT THE FISH?

ABSTRACT It is understood that operational and structural conditions at hydroelectric facilities along with environmental conditions of the migration corridors affect the passage conditions for fish. Hydropower fish survival assessments at the individual- and population-level have progressed over the past decade with development of turbine simulation software and improvements in telemetry systems, in particular, micro-transmitters, cabled and autonomous receivers, and advanced statistical designs that provide precise estimates of passage routes and dam-passage survival. However, these approaches often ignore fish condition as a variable in passage and survival analyses. To account for fish condition effects on survival results, compliance statistical models often require increased numbers of tagged fish. For example, prior to and during migration, fish encounter numerous stressors (e.g., disease, predation, contact with structures, decompression events), all of which can cause physical and physiological stress, altering the probability of survival after passage through a dam or a series of dams. In addition, the effects of surgical transmitter implantation process or the transmitter itself may cause physiological stress, alter behavior, and/or decrease survival. Careful physiological evaluations can augment survival model assumptions, resultant data, and predictive scenarios. To exemplify this, surgeons concurrently noted fish condition and surgical implantation during a multi-dam compliance study in 2011. The analyses indicted that surgeon observations on fish condition and surgical outcomes were related to 24 h holding mortalities and fish that never detected after release. Short reach and long reach survival were related to surgical outcomes and fish condition, respectively.

hydropower, hydrokinetics,↗

DOE OSTI2017

Energy Assessment of Automated Mobility Districts

Automated vehicles (AVs) are increasingly being discussed as the basis for on-demand mobility services, introducing a new paradigm in which a fleet of AVs displace private automobiles for day-to-day travel in dense activity districts. This project examines such a concept to displace privately owned automobiles within a region containing dense activity generators (jobs, retail, entertainment, etc.), referred to as an automated mobility district (AMDs). The project reviews several such districts including airport, college campuses, business parks, downtown urban cores, and military bases, with examples of previous attempts to meet the mobility needs apart from private automobiles, some with automated technology and others with more traditional transit based solutions. The issues and benefits of AMDs are framed within the perspective of intra-district, inter-district, and border issues, and the requirements for a modeling framework are identified to adequately reflect the breadth of mobility, energy, and emissions impact anticipated with AMDs.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Coordination of thermostatically controlled loads

Apparatus and methods for a market-based control framework to coordinate a group of autonomous thermostatically controlled loads (TCL) to achieve system-level objectives with pricing incentives is disclosed. In one example of the disclosed technology, a method of providing power to a load via a power grid by submitting bids to a coordinator includes determining an energy response relating price data for one or more energy prices to quantity data for power to be consumed by the load, sending a bid for power for a finite time period based on the energy response to the coordinator, and receiving a clearing price based on: the bid, on bids received from a plurality of additional loads, and a feeder power constraint. In some examples, the energy response is based at least in part on an equivalent thermal parameter model and a control policy indicating one or more power states for the load.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2023

Coordination of thermostatically controlled loads

Lian, Jianming↗

DOE OSTI2020

Panel Session 100: An Integrated Systems-Based Monitoring Approach

This panel focused on recent advancements in monitoring and remaining challenges associated with the implementing cost-effective monitoring approaches for complex DOE sites. Monitoring presents one of the largest, long-term costs for environmental remediation and site stewardship within the DOE because contamination at complex sites will likely be present for tens to hundreds of years into the future. Integration of monitoring with predictive modeling is an approach that can reduce long-term monitoring costs as well as support the effective monitoring design and data interpretation for managing a site toward closure and long-term stewardship. Panelists with presentations: Autonomous Electrical Geophysical Monitoring of Contaminated Sites (Tim Johnson); Systems Based Long-Term Monitoring for DOE Legacy Management (David Shafer); Long-Term Systems Based Monitoring (Anderson Ward)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗