SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
HOPE is an MPC-based, optimized and robust controller algorithm developed for the energy and power management of DC distributed EV charging hubs.
In this paper we propose a reinforcement learning (RL) algorithm for path planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to UAV path planning problems are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs need to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health, and off-nominal battery power consumption. Machine learning methods such as reinforcement learning (RL) have the potential to revolutionize how vehicles navigate in such uncertain environments. In this study, we compute UAV trajectories from a pre-determined starting position to a target cell within a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption and operation time. The UAV navigates the grid by taking actions to move in any of the eight cardinal and inter-cardinal directions, under constant thrust profile. The resultant UAV state is sampled from a probability distribution which accounts for the UAV’s action, local wind velocity, and the presence of obstacles or boundaries. As the unmanned airspace gets more complex due to multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance need to be made. Our Markov Decision Process (MDP) environment model can capture any combination of these in the optimization objective, making it novel compared to other work in the field.
Design of TES microcalorimeters requires a tradeoff between resolution and dynamic range. Often, experimenters will require linearity for the highest energy signals, which requires additional heat capacity be added to the detector. This results in a reduction of low energy resolution in the detector. We derive and demonstrate an algorithm that allows operation far into the nonlinear regime with little loss in spectral resolution. We use a least squares optimal filter that varies with photon energy to accommodate the nonlinearity of the detector and the non-stationarity of the noise. The fitting process we use can be seen as an application of differential geometry. This recognition provides a set of well-developed tools to extend our work to more complex situations. The proper calibration of a nonlinear microcalorimeter requires a source with densely spaced narrow lines. A pulsed laser multi-photon source is used here, and is seen to be a powerful tool for allowing us to develop practical systems with significant detector nonlinearity. The combination of our analysis techniques and the multi-photon laser source create a powerful tool for increasing the performance of future TES microcalorimeters.
Demand-side management in the buildings is essential for meeting grid flexibility needs in a highly renewable energy scenario. Appliance load monitoring helps decision making for demand-side management by providing the information on operation status/power consumption from different appliances in the buildings. Nonintrusive load monitoring (NILM) is an attractive option for appliance load monitoring using because it has lower cost for sensors and helps mitigate privacy concerns. In this study, the team used an event detection technique followed by two different methods for event classification. The results from k-means clustering showed that the events from a single appliance are often distributed in multiple clusters. Thus, the unsupervised method of NILM using k-means clustering used in this study was not very suitable for load disaggregation. The results from NILM showed that the F1 score for event classification was 0.77 for a heat pump water heater and very low for other appliances using the rule-based classification.
Vehicle connectivity and automated driving technologies individually have the potential to decrease energy consumption and/or increase safety on light, medium or heavy duty vehicles to varying degrees depending on the traffic infrastructure and specific driving scenarios. Due to advances in sensing, perception and computing power, research and development emphasis in the mobility sector has shifted away from connectivity. Prior research has shown that driving automation with the absence of connectivity can in certain circumstances increase energy consumption. The effectiveness of synergizing connectivity and driving automation technologies is the focus of this work, specifically applied to vehicle cohorts of mixed composition, light and heavy duty, and powertrains ranging from all electric to conventional internal combustion engine. The project team is led by Michigan Technological University (MTU) and partnered with AVL Mobility Technologies Inc. (AVL), Borg Warner (BW), Traffic Technology Services (TTS), American Center for Mobility (ACM) and Navistar (NAV). The main thrusts for the team are to develop a micro-traffic simulation environment with specific VD&PT system attributes and CAV capabilities, 2) field a vehicle test fleet of mixed classification, propulsion and CAV capacity, 3) develop artificial intelligence (AI) and machine learning (ML) based multi-agent optimization methods for various traffic infrastructures, 4) integrate the virtual environment and the optimization methods then deploy the system as a CAV hardware in the loop (HiL) for the vehicle test fleet and 5) conduct closed track and public road testing to validate simulation and demonstrated energy and mobility improvements at multiple scales. For a cohort of mixed vehicles, the team will demonstrate a reduction of energy consumption of 10-50% at intersection, arterial roadway and limited access highway scenarios through connectivity and automation in simulation and at a closed test track. The energy reduction objectives of the project are summarized in Table 1, indicating the infrastructure and over what distances are relevant considered. Single scenario energy reductions are not relevant and thus, the research team took the approach to vary parameters associated with the infrastructure, vehicle cohort composition and dynamic behavior to generate energy consumption distributions for both unconnected and connected scenarios.
Efficient light management is key to maximizing power conversion efficiency (PCE) in monolithic perovskite/silicon tandem solar cells. Achieving peak efficiency requires closely matched current generation in all junctions, especially in integrated configurations. However, real-world conditions vary significantly due to factors such as sunlight spectrum, diffuse-to-direct sunlight ratio, angular distribution of light, subcell temperature coefficients, and ground reflection. This study introduces a comprehensive optical and device simulation to optimize perovskite/silicon tandem cells, considering experimental luminescent coupling (LC) efficiency and its dependence on working conditions, alongside variations in radiative recombination, effect of temperature on absorptivity spectra, and cloud cover. Our results show potential energy yield improvements of up to 1.4 % with LC, based on current perovskite radiative recombination records, and up to 4 % with direct bandgap materials. Although radiative recombination's dependence on excitation intensity reduces output power and requires thicker absorbers, LC compensates for these losses. LC also lowers the optimized bandgap for the perovskite top cell from 1.72 eV to 1.64–1.68 eV, or even lower in regions with redshifted irradiance. Additionally, optimization revealed that thinner silicon bottom cells require a higher perovskite top cell bandgap, impacting the balance between fabrication cost and cell stability.
The U.S. pulp and paper industry is the third-largest energy consumer in manufacturing, accounting for roughly 10% of sector energy use. Improving energy efficiency reduces operating costs and strengthens competitiveness. To support this effort, the U.S. Department of Energy (DOE), through Oak Ridge National Laboratory (ORNL), launched the Energy Intensive Industries (EII) Initiative. A two-year pilot across 45 industrial sites identified more than 4 trillion Btu/year in potential energy savings. This presentation outlines plans for a follow-up technical assistance program tailored to pulp and paper mills. Available resources include a cost-savings scoping tool, implementation planning guidance, and technical support for applying advanced methods such as Pinch Analysis for integrated process-utilities optimization. The session introduces key Pinch Analysis principles and highlights case studies demonstrating measurable improvements. ORNL also seeks industry feedback on barriers to efficiency improvements, including technology gaps and resource needs. DOE’s broader objective is to accelerate productivity and economic competitiveness across U.S. energy-intensive industries.
Ontario International Airport Authority (OIAA)'s Electric Vehicle (EV) Blueprint project presentation focuses on the possibilities for fleet electrification, including medium heavy duty charging and hydrogen refueling and their associated planning, infrastructure, operation, and maintenance options. This report includes detailed list of available electric ground support equipment (GSE), description of NREL tools for EV infrastructure design and optimization, and analyses of the current fleet and the potential grid impacts of electrification. Key recommendations are made in regard to simulation-based and in-depth system analyses to identify optimal charging infrastructure and long-term grid stability, and to ensure that the selected station architecture will provide enough space and capacity for future expansion and resiliency.
Not Available
Not Available
Not Available
Explore the source record for details and available documents.
This project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at commercial buildings located in Saint Paul, MN; Madison, WI; New York City; and Fort Worth, TX.
Poster of results that can be generated from the price taker class within the IDAES ecosystem. Data analysis for the comparison of emerging energy generation technologies over historical and projected marginal price scenarios are shown, and viability of solid oxide energy systems is concluded.
Explore the source record for details and available documents.
Integrated Utility Systems (IUS) have been suggested as a means of reducing the cost and conserving the nonrenewable energy resources required to supply utility services (energy, water, and waste disposal) to developments of limited size. The potential for further improving the performance and reducing the cost of IUS installations through the use of energy storage devices is examined and the results are summarized. Candidate energy storage concepts in the general areas of thermal, inertial, superconducting magnetic, electrochemical, chemical, and compressed air energy storage are assessed and the storage of thermal energy as the sensible heat of water is selected as the primary candidate for near term application to IUS.