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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Special Section on Local and Distributed Electricity Markets

Driven by the goals of clean energy and zero carbon emissions, the power industry is undergoing significant transformations. The rapid growth of diverse distributed energy resources (DERs) at grid edge such as rooftop photovoltaics (PVs) and electric vehicles is transforming the traditional centralized power grid management to a decentralized, bottom-up, and localized control paradigm. Establishing local and distribution-level electricity markets provides an effective solution to managing large amounts of small-scale DERs. New regulations such as the recent FERC Order 2222 in the U.S. open the door to DERs in the wholesale markets. Through coordinating the local and distribution-level markets with the transmission-level wholesale market, the DERs and prosumers can trade energy and flexibility locally with each other and meanwhile provide energy, flexibility and ancillary services to the bulk power grid. During this transition, there are many new technical challenges to address, calling for innovative ideas and interdisciplinary research in this promising direction. Advanced information and communication technologies (ICT) are needed, as a key enabler, for the development and practical implementation of local and distribution electricity markets. Research into local and distribution markets is strongly interdisciplinary, involving the state of the art in power engineering, economics, and digital/information technology. A broad spectrum of contributors from universities, industry, research laboratories and policy makers is sought to develop and present solutions and technologies that will facilitate and advance practical applications and implementations of local and distribution-level electricity markets to uncover the values of DERs.

distributed energy resources↗
DOE OSTI2021

Market Brief: Customer-sited Distributed Energy Resources

This market brief summarizes key takeaways from interviews with vendors and service providers in one or more areas of the DER market: DER controls, battery storage, solar photovoltaic, thermal energy storage, combined heat and power, flexible building loads, and electric vehicle charging. Back-up generators used only in emergencies are not in the scope of this market brief.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Synthesis of Hf 0.75 Ta 0.25 B 2 for self-coating TPS

Ultra-high temperature ceramic materials (UHTCs) are important for designing high-performance aerospace vehicles that can withstand repeated exposures to high temperatures. UHTCs mixed with silicides often have well-controlled oxidation due to the formation of protective silicates, which create a regenerative outer protective layer. Borides are known to have high melting points, high hardness and reasonably good oxidation resistance. In this study, Hf 0.75 Ta 0.25 B 2 (HTB) is proposed as a potential alternative to YSZ protective coatings and ZrB 2- SiC composites via the formation of Hf 6 Ta 2 O 17 (HTO) passivation layer. The objective of this paper is to explore the parameter space of HTB synthesis via borocarbothermal (BCTR). Effects on particle size, phase purity, and residual oxygen content were analyzed with parameters of atmosphere composition, reactant grain sizes, and differing reaction pathways. The BCTR of HfO 2 and Ta 2 O 5 were analyzed to predict HTB behavior. It was shown that both one-step and two-step reaction routes can yield HTB, but two-step yields a purer product. Nano B 4 C produced finer HTB and facilitated reaction completion. Using a reducing atmosphere also enhanced reaction completion

36 MATERIALS SCIENCE↗
DOE OSTI2025

Demonstration of Utility Managed Smart Charging for Multiple Benefit Streams (Final Report)

In the summer of 2020, the U.S. Department of Energy (DOE) awarded funding to Exelon’s Maryland utilities—Baltimore Gas and Electric (BGE), Delmarva Power & Light (DPL), and Potomac Electric Power Company (Pepco)—to implement the Smart Charge Management (SCM) pilot. This initiative aimed to design and implement managed electric vehicle (EV) charging strategies, evaluate the grid impacts of EV charging, and assess the utilities' ability to control EV load based on real-time grid conditions. The SCM pilot explored four aspects for continued improvement: (1) cybersecurity and managed charging functionality testing of two vendor platforms—WeaveGrid (telematics-based) and Shell Recharge Solutions (network-based)—which pursued charge scheduling and optimization through distinct approaches; (2) an analysis by Argonne National Laboratory (ANL) modeling team of three potential SCM enrollment scenarios within BGE and Pepco service territories over the next decade to assess future scalability; (3) employing customer engagement strategies, including surveys and a responsive pricing approach; and (4) the launch and implementation of pilots in Exelon’s Maryland territories in collaboration with WeaveGrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Misalignment Tolerant Three-phase Wireless Fast Charging System for Electric Vehicles

This project proposes improving the technology level of the three-phase wireless charging technology developed at Oak Ridge National Laboratory (ORNL). Successful commercialization of wireless charging systems require that they meet strict safety standards for electromagnetic field emissions and interoperability with other wireless charging technology. ORNL has previously demonstrated a 50kW three-phase wireless power transfer system with 95% efficiency. This prototype system weighed less than 50% of similar high-power wireless charging system while simultaneously exhibiting better safety characteristics. Due to better material utilization, the reduction in mass is expected to translate into similar cost reduction for the magnetic coupler. Additionally, the smaller footprint of the system can ease vehicle integration difficulties that would be encountered using other technology. Furthermore, the three-phase design can be made to operate with existing single-phase coupler design more easily and without intentional physical misalignment which would otherwise pose safety issues.This project developed ORNL’s WPT technology to satisfy the specifications and requirements for electric vehicle (EV) charging. The result of the project will be a prototype and reference design for a high-power wireless charging system—including magnetics, power electronics, and controls—serving as a baseline for product development.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Charging Infrastructure Technologies: Development of a Multiport, >1 MW Charging System for Medium- and Heavy-Duty Electric Vehicles

Development of a Multiport, >1 MW Charging System for Medium- and Heavy-Duty Electric Vehicles project will develop research tools for a framework to design, optimize, and demonstrate key components of a multi-port 1+ MW medium-voltage connected charging system. The objectives of this effort are to develop strategies and technologies for multi-port 1+ MW grid-connected stations to recharge MD/HD electric vehicles at fast-charging travel plazas or at fleet depots; through industry engagement, charging station utilization and load analysis, grid impacts and interconnection analysis, detailed power electronics component design and controller demonstration, site and battery charge control design and controller demonstration; and charging connector design.

1+MW charging↗
DOE OSTI2021

Thermodynamic modeling of hydrogen fueling process from high-pressure storage tank to vehicle tank

This study develops a hydrogen fueling station (HFS) thermodynamic model that simulates the actual fueling process in which hydrogen is supplied from a high-pressure (HP) storage tank into a fuel cell electric vehicle (FCEV) tank. To make the model as accurate as possible, we use the same components and specifications as in actual HFSs, such as a pressure control valve, a pre-cooling system, and an FCEV tank. After the components and their specifications are set, pressure and temperature profiles are set as the HP tank supply conditions. Based on the pressure and temperature profiles, the model solves for the temperature, pressure, and mass flow rate of hydrogen at each downstream position, including the inside of the vehicle tank. The values predicted by the model are compared with experimental data, and we show that the developed model makes it possible to accurately simulate those values at any position during the fueling process.

08 HYDROGEN↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

42 ENGINEERING↗
DOE OSTI2024

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

actuated signal control↗
DOE OSTI2023

Controller Hardware-in-the-Loop Modeling and Operation of a High-Power DC Charging Hub

High-power charging (HPC) concept will be a critical enabler for the wide-scale adoption and integration of electric vehicles (EVs). HPC hubs will have a significant impact on achieving this goal, particularly when considering the charging requirements of next-generation medium and heavy-duty vehicles (MD/HD) used in various vocational scenarios. This emphasizes the significance of evaluating and confirming the technical planning and multi-level operation of HPC hubs through modeling, protocol implementation, and simulation before hardware demonstration. The focus of this study is to develop the architecture, modeling, communication, and real-time controller hardware-in-the-loop (C-HIL) simulation of HPC hubs using the DC distribution system approach. Specifically, this work demonstrates the practical implementation of a custom Smart Energy Management System (SEMS) for a real-time C-HIL HPC hub simulation using the Open Charge Point Protocol (OCPP) for communication. OCPP is implemented on the Node-Red framework and the custom controller is executed in Python. We developed a rule-based controller to demonstrate the proposed approach.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network

Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.

Kumarasamy, Vijayalakshmi K [The University of Ten↗
DOE OSTI2023

Semi-Automatic Geographic Information System Framework for Creating Photo-Realistic Digital Twin Cities to Support Autonomous Driving Research

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. Here this paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗
DOE OSTI2020

Status and challenges for the application of platinum group metal-free catalysts in proton-exchange membrane fuel cells

Platinum group metal-free (PGM-free) electrocatalysts are an important class of materials for the sustainable energy economy and significant effort has been made towards developing PGM-free catalysts to replace their costly Pt counterparts in fuel cell electric vehicles. The progress made in activity over the last decade is reviewed, along with synthesis strategies for more controlled active site formation and performance gains achieved through electrode engineering. The increased focus on durability, the growing understanding of degradation mechanisms, and the need for standard performance and accelerated stress test protocols are discussed.

25 ENERGY STORAGE↗
DOE OSTI2021

Quality control and crop characterization framework for multi-temporal UAV LiDAR data over mechanized agricultural fields

Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Finally, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data-based and secure switched cyber–physical systems

In this work, we develop a completely model-free moving target defense framework for the detection and mitigation of sensor and/or actuator attacks in cyber–physical systems with dynamics that evolve in discrete-time. We incorporate an intrusion detection mechanism based on an approximate dynamic programming technique that learns the policies for optimal regulation and optimal tracking while simultaneously defending against actuator and sensor attacks in a model-free fashion. Switching rules are leveraged to force proactive and reactive defense mechanisms as well as, guarantee the stability of the equilibrium point. Finally, as a case study, we apply the proposed moving target defense framework to a DC–DC converter that is used in electric vehicles.

42 ENGINEERING↗
DOE OSTI2022

Temperature-Dependent Fracture Resistance of Silicon Nanopillars during Electrochemical Lithiation

During the lithation of silicon anodes, the solid-state diffusion of lithium into Li x Si follows the Arrhenius law, the resulting morphology and fracture behavior are determined by the silicon anode operation temperature. Here, we reveal the temperature dependence of the lithiation mechanics of crystalline silicon nanopillars (SiNPs) via microscopic observations of the anisotropic growth and fracture behavior. In this work, we fabricated 1D SiNP structures with various orientations ($\langle100\rangle, \langle110\rangle$, and $\langle111\rangle$) as working electrodes and operated them at temperatures ranging from –20 to 40 °C. The lithiation of crystalline silicon at low temperatures exhibited preferential volume expansion along $\langle110\rangle$ and decreased fracture resistance. Furthermore, low temperatures caused the catastrophic fracture of amorphous silicon after the second lithiation. Our findings demonstrate the importance of silicon anode temperature control to prevent mechanical fracture during the cycle of lithium-ion batteries in harsh environments (e.g., electric vehicles in winter).

25 ENERGY STORAGE↗
DOE OSTI2022

What Would It Take to Manufacture Perovskite Solar Cells in Space?

Imagine: Astronauts land on the moon. They verify their arrival with mission control and perform system checks and validations. After the dust settles, they open the airlock of the landing vehicle and venture outside. A side hatch opens, and a flexible substrate slowly unfurls on a boom. A series of printer heads raster, hovering over the substrate and sequentially vapor-depositing the constituent layers of a perovskite solar module. In time, a 1-megawatt array has been manufactured on the moon and can now be connected to supply power to the Artemis Base Camp. This ambitious vision could someday become a reality.

14 SOLAR ENERGY↗