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DOE OSTI2021

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Light-Duty Vehicle Choice Modeling and Benefits Analysis (van018)

The U.S. Department of Energy’s Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. The analysis in this poster is based on technical progress goals established in VTO and HFTO in the years immediately prior to and including 2020, and it summarizes the estimated energy and emissions benefits corresponding with achievement of those goals. The goals span research activities on batteries, electric drive technologies (EDT), combustion, lightweight materials, fuel cells, and hydrogen storage. The Automotive Deployment Options Projection Tool (ADOPT) is used to estimate the benefits for light-duty vehicles. ADOPT is a vehicle choice and stock model that estimates vehicle technology improvement impacts on sales, energy, and emissions. It includes all the existing vehicle options for realism, estimates their sales using extensively validated consumer preferences, creates new market-driven vehicle options through time, and rolls up sales to estimate energy and emissions. ADOPT takes in technology progress assumptions and applies these to the modeled vehicles through time. The assumptions are represented by a No Program scenario that reflects the technology improvements assumed to occur without further contributions from VTO or HFTO, and a Program Success scenario under which VTO and HFTO program goals are realized. The benefits are calculated by comparing ADOPT's estimated national-level energy and emissions resulting from the Program Success relative to the No Program scenario. By 2050, the Program Success scenario results in 11% less annual petroleum consumption and 10% less annual carbon emissions than the No Program scenario.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Vehicle Technologies and Hydrogen and Fuel Cell Technologies Research and Development Programs Benefits Assessment Report for 2020

The U.S. Department of Energy’s Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. VTO and HFTO regularly revisit and update relevant research and development goals and areas of emphasis in response to the latest technological advancements and in alignment with current national priorities. As such, analyses of expected benefits resulting from VTO and HFTO investments and anticipated goal achievements are updated periodically and will be again for 2021 in the context of the latest national-level transportation decarbonization goals. The analysis in the present report is based on technical progress goals established in VTO and HFTO in the years immediately prior to and including 2020, and it summarizes the estimated energy and emissions benefits corresponding to achievement of those goals. The goals span research activities on batteries, electric drive technologies (EDT), combustion, lightweight materials, fuel cells, and hydrogen storage. The evaluation includes detailed analyses into the benefits of technology improvements on the U.S. light-duty (LD) vehicle fleet and separately on the U.S. medium- and heavy-duty (MDHD) vehicle fleet. This report summarizes the outcomes from each of these analyses both independently and in combination.

08 HYDROGEN↗

DOE OSTI2020

Corrosion resistance of functionally graded TiN/Ti coatings for proton exchange membrane fuel cells

Bipolar Plates (BPP) are important components of proton exchange membrane fuel cell (PEMFC) stacks. In the development of innovative fuel cell designs, it is advantageous to use aluminum for these applications, however, this material lacks the necessary corrosion resistance. Since the performance of PEMFC stacks depends on BPP properties, in particular, corrosion resistance, depositing titanium nitride (TiN) thin films onto aluminum substrates may improve their efficiency and durability. Present work focuses on improving corrosion resistance and hydrophobicity of TiN/Ti by using N graded films deposited onto aluminum substrates (AA-1100) by grid-assisted magnetron sputtering (GAMS). Electrochemical impedance spectroscopy (EIS) with potentiodynamic and potentiostatic polarization are used to investigate the performance of the substrate/film system at room temperature and 70 oC, thus simulating a prototypic PEMFC electrolyte environment. Electrochemical test results showed that graded TiN films improved corrosion resistance when compared with both the homogeneous films and the AA1100 uncoated substrate. Furthermore, contact angle results reveal improved hydrophobicity for both homogeneous and graded TiN coatings when compared with the AA1100 substrate.

25 ENERGY STORAGE↗

DOE OSTI2022

Electroprecipitation Mechanism Enabling Silica and Hardness Removal through Aluminum-Based Electrocoagulation

We evaluate the effectiveness of an aluminum-based electrocoagulation pretreatment system to remove dissolved silica and hardness. Silica and hardness limit water recovery during membrane-based desalination applications when silica and hardness exceed the solubility limit and generate scale on the membrane surface. We show that simultaneous removal of nearly all silica (95 ± 4%) and a significant amount of hardness (40–60%) occurs with a hydraulic residence time of 2 h and a charge loading between 0 and 1200 C/L. Increasing the residence time maximized the hardness removal (58 ± 8%) via the formation of larger flocs, which allowed for more constituent removal by gravity settling. We highlight the trade-offs between improved energy efficiency at lower charge loadings and an improved removal rate at a higher charge loading. We further compare the percentage of silica and hardness removed in multicomponent solutions and compare this to single component feed solution. We discuss the implications that operational considerations have in terms of cost and treatment capacity. Finally, a cost–benefit analysis comparing chemical coagulation with electrocoagulation indicates that electrocoagulation could be half the cost of chemical coagulation and could produce more stable effluent pH and conductivity.

36 MATERIALS SCIENCE↗

DOE OSTI2021

High-Accuracy Simulations to Model Pyrometallurgical Processes in a Secondary Lead Reverberatory Furnace

The US manufacturing industry produces about 1.3 million tons of refined lead each year using secondary sources consisting mainly of lead batteries. ORNL is partnering with Gopher resource, the second largest lead recycling company in the United States, and GTI, to develop a high-fidelity CFD model of a directly fired, reverberatory-style, secondary lead furnace. These High Performance Computing (HPC) simulations are aimed to use first principles modeling for combustion and melting processes of the secondary lead feed while accounting for complex interphase interactions between the gas, solid charge (lead) material, slag, and metal phases. Through validation against operating plant data, this effort will enable significant improvements in design, operational parameters, and energy efficiency, thus improving productivity and refractory lifetime of secondary lead melting furnaces. Estimated savings/reduction of, at least, 1 trillion BTU, 1 million ton/year of greenhouse gas emissions, and $\$50$ million/year to the US lead industry can be expected. ORNL resources and expertise in high-performance computing and multicomponent, multiphase flows were utilized to realize this goal while advancing the understanding of the smelting and melting processes occurring within the furnace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗

DOE OSTI2020

Light-Duty Vehicle Choice Modeling and Benefits Analysis

The U.S. Department of Energy's Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. This presentation steps through the approach, updates to the approach, and preliminary estimates of light-duty vehicle energy and emissions benefits from the continuation and success of VTO and HFTO programs. The programs include research on batteries, electric drive technologies (EDT), combustion, materials, fuel cells, and hydrogen storage. Preliminary results for battery and EDT program success show an annual 24% reduction in petroleum use and 13% reduction in carbon emissions.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Vehicle and Mobility Technologies 2021 Annual Impact Report

Research and development at the National Renewable Energy Laboratory supports the U.S. Department of Energy's Vehicle Technologies Office in advancing efficient and sustainable transportation technologies that will improve energy efficiency, fuel economy, and mobility energy productivity, and will enable America to achieve a resilient, secure, clean, and affordable transportation future.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Vehicle and Mobility Technologies 2022 Annual Impact Report

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Vehicle Technologies Fiscal Year 2023 Annual Report

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Maximum-impact Adversary Design for Network-based Control System: A Case Study on Grid-interactive Efficient Buildings

The Internet of Things (IoT) technology has dramatically improved the efficiency of today's building operation and management. By connecting controllable devices into a communication network, control signals can be easily passed to the devices, and operating status can be acquired from measurable ends with minimal effort. However, this all-connected configuration could also expose the network-based control system (NBCS) to malicious actions, such as cyberattacks. One of the common NBCSs is the building automation system. With the promotion of grid-interactive efficient buildings (GEBs), there has been increasing attention on securing the buildings from the network perspective. This research proposes a maximum-impact adversary design framework so that the adversary can provide the most adversarial impact on the controlled system while remaining stealthy. The proposed framework is numerically demonstrated on a network-based building energy and control system. The building energy system is built in a Modelica-based simulation environment and controlled by the state-of-the-art ASHRAE Guideline 36 control sequences. The control commands at the supervisory level, generated from the Guideline 36 controller, are assumed to be sent to local devices through communication networks using the BACnet protocol. Simulation results show that the proposed maximum-impact adversary on such a system can stealthily affect the building system's performance to its maximum extent. It is anticipated that results can be used by researchers and practitioners in the building automation industry to design efficient and robust cyber-attack detection algorithms, especially for stealthy attacks.

Chu, Mengyuan↗

DOE OSTI2024

Vehicle and Mobility Technologies 2024 Annual Impact Report

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2026

Leveraging Multiproton-Coupled Electron Transfer to Improve Ir(III) Photocatalyst Efficiency

In photoredox reactions, charge recombination (CR) limits quantum yields, hindering the efficient conversion of light energy into catalytic activity. To address this, we drew inspiration from redox relays in photosystem II (PSII) and developed a new series of iridium(III) complexes featuring covalently attached benzimidazole-phenol-pyridine (BIP-Py) groups to facilitate intramolecular multiproton-coupled electron transfer (MPCET). Herein, we evaluate the effects of MPCET through an extended and well-defined hydrogen-bond network to improve photocatalytic activity and mitigate rapid charge recombination. Infrared spectroelectrochemistry reveals pyridine protonation upon phenol oxidation, while visible spectroelectrochemistry and transient absorption spectroscopy confirm the electro- and photochemical formation of chargeseparated states (CSS) involving oxidized BIP, resulting from intramolecular proton-coupled electron transfer (PCET). The application of the BIP-Py platform in a photocatalytic Nhydroxyphthalimide ester reduction reaction resulted in a ∼106-fold reduction in CR rate and a quantum yield enhancement of up to 157%. Our findings suggest that incorporating MPCET-based redox relays into photocatalyst frameworks is an effective strategy to enhance the efficiency of photocatalytic systems.

Catalysts↗

DOE OSTI2024

A Step toward Process Electrification: Microwave-Assisted Co-Gasification of Waste Plastics and Biomass to Produce Clean Energy

Barely 9% of the 353 million metric tons of plastic waste produced worldwide over the last two decades has been recycled, with the majority of it being either landfilled or incinerated. Recycling and upcycling plastic waste is thus essential in mitigating environmental pollution. Gasification is the thermochemical conversion of solid carbon feedstock at high temperatures using a gasifying agent to produce hydrogen-rich syngas. Existing gasification technologies are energy-intensive and require high operating temperatures (>1000 °C), leaving them uncommercial. Microwave heating has sparked attention in a variety of chemical processes due to its specific advantages over traditional heating technologies, such as rapid and selective heating to improve process efficiency. NETL’s research team recently investigated the synergistic advantages of corn stover and mixed plastics in producing hydrogen-rich syngas via microwave heating. As a continuation of the research, this study provides an outlook over the non-catalytic versus catalytic effects of plastic-corn stover gasification via process electrification to enhance H2 production, minimize undesired tar, and improve process efficiency. Our aim is to provide complete electrification of the existing gasification process by using waste plastics as a starting block to produce valuable fuel and support the nation’s net zero goals.

Abedin, Ashraf↗

DOE OSTI2024

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗

DOE OSTI2021

Optical Characterization of Defects in High-Efficiency (Ag,Cu)(In,Ga)Se2

We applied time-resolved photoluminescence (TRPL) spectroscopy to study optimized chalcopyrite (Ag,Cu)(In,Ga)Se2 thin films. The device shows power conversion efficiency of 18.7%. The metastable defect VSe-VCu within ACIGS at EV+0.98 eV is detected in sub-bandgap TRPL excitation spectra. TRPL lifetime of 50 ns is limited by the density of mid-gap defects such as CuGa or CuIn. The similarity of TRPL dynamics before and after light soaking indicates the optimized ACIGS thin film is less metastable because the density of VCu-VSe defect is reduced to below 10^15 cm^-3. This study indicates that ACIGS has improved cell efficiency and reliability characteristics.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗