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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

A Concept of Operations for Power Beaming of Electric Air Vehicles

A concept of operations is presented for power beaming of microwaves to extend battery charge of an electric air vehicle. Critical aspects of airspace operations and power beam charging are addressed. Three classes of electric Vertical Take-Off and Landing vehicle models are considered for recharging. The effects of transmitter frequency, antenna size, and beam width are analyzed. Based on past experiments in the industry, a 2.5 kW power beam is selected. A no-drag rectifying antenna under the belly of the vehicle is selected along with the conversion efficiency, power density, and its size. The combined efficiency of the transmit/receive system as well as the beam containment values are addressed. These parameters are chosen to assess viability and feasibility of the energy augmentation method to establish safety for humans on-board from exposure to the transmitted energy. NASA will test the concept of operations in a laboratory experiment with parameters selected and presented here.

eVTOL vehicles↗
NASA NTRS2011

Preliminary Investigation on Battery Sizing Investigation for Thrust Vector Control on Ares I and Ares V Launch Vehicles

An investigation into the merits of battery powered Electro Hydrostatic Actuation (EHA) for Thrust Vector Control (TVC) of the Ares I and Ares V launch vehicles is described. A top level trade study was conducted to ascertain the technical merits of lithium-ion (Li-ion) and thermal battery performance to determine the preferred choice of an energy storage system chemistry that provides high power discharge capability for a relatively short duration.

Miller, Thomas B.↗
NASA NTRS2024

Performance of Two Battery Prognostic Applications used by Two Octocopters for Safe Low Altitude Autonomous Flight Operations

This paper addresses the problem of building trust in online predictions of the remaining available flying time for two different electric Unmanned Aerial Vehicles (eUAVs) powered by lithium-ion-polymer batteries. Flight tests for various automation research missions for the two vehicles were monitored using two on-board battery health management applications to make predictions of the remaining flying time (RFT) for each eUAV and to predict the state of the battery. Playback of the voltage, current and temperature profiles of the battery discharge were used to assess the accuracy of the estimation of the voltage and the charge states of the models as well as the estimate of the RFT. The reference ground truth values were the observed landing time and the measured battery pack resting pack voltage 20 minutes after the flight. The predicted RFT, state of charge (SoC), and state of energy (SoE) were compared with the observed results. Noise values of one standard deviation from the mean values of the internal charge states of the battery model during a reference run were used to vary the states during simulation. One application used an equivalent circuit model of the electrical dynamics of the battery pack, and the other application used a reduced-order electrochemistry model. The variation of the model state components was compared to the variation in the estimate of the RFT and the variation in the SoE to estimate a confidence factor. Variation in the estimates caused by factors affecting the off-line laboratory parameter identification experiments is considered. Variation in the estimates due to environmental factors are discussed.

Assurance↗
NASA NTRS1968

Zinc-oxygen primary cell yields high energy density

Zinc-oxygen primary cell yields high energy density for battery used as an auxiliary power source in space vehicle systems. Maximum reliability and minimum battery weight is achieved by using a stacking configuration of 23 series-connected modules with 6 parallel-connected cells per module.

Graff, C. B.↗
NASA NTRS1997

Ultra-Capacitor Energy Storage in a Large Hybrid Electric Bus

The power requirements for inner city transit buses are characterized by power peaks about an order of magnitude larger than the average power usage of the vehicle. For these vehicles, hybrid power trains can offer significantly improved fuel economy and exhaust emissions. A critical design challenge, however, has been developing the energy storage and power management system to respond to these rapid power variations. Most hybrid vehicles today use chemical energy storage batteries to supplement the power from the fuel burning generator unit. Chemical storage batteries however, present several difficulties in power management and control. These difficulties include (1) inadequate life, (2) limited current delivery as well as absorption during regenerative braking, (3) inaccurate measurement of state of charge, and (4) stored energy safety issues. Recent advances in ultra-capacitor technology create an opportunity to address these concerns. The NASA Lewis Research Center, in cooperation with industry and academia, has developed an advanced hybrid electric transit bus using ultra-capacitors as the primary energy storage system. At over 15,000-kg gross weight, this is the largest vehicle of its kind ever built using this advanced energy storage technology. Results of analyses show that the vehicle will match the performance of an equivalent conventionally powered vehicle over typical inner city drive cycles. This paper describes the overall power system architecture, the evolution of the control strategy, and analysis of power flow and vehicle performance.

Viterna, L. A.↗
NASA NTRSDate not supplied

Energy-Optimized Path Planning for Uas in Varying Winds Via Reinforcement Learning

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.

trajectory planning↗
NASA NTRS1973

Wind power demonstration and siting problems

Technical and economic feasibility studies on a small windmill to provide overnight charging for an electrically driven car are reported. The auxiliary generator provides power for heating and cooling the vehicle which runs for 25 miles on battery power alone, and for 50 miles with the onboard charger operating. The blades for this windmill have a diameter of 12 feet and are coupled through to a conventional automobile alternator so that they are able to completely recharge car batteries in 8 hours. Optimization of a windmill/storage system requires detailed wind velocity information which permits rational sitting of wind power system stations.

Bergey, K. H.↗
NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs 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 Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on 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 either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗
NASA NTRSDate not supplied

Usage-based Lifing of Lithium-Ion Battery with HybridPhysics-Informed Neural Networks

Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs).The ability to model and forecast the remaining useful life of these batteries enables UAV reliability assurance. Building accurate models for battery state of charge and state of health based on first principles is challenging due to the complex electrochemistry that governs battery operations and computational complexity required to solve them. Therefore, reduced order models are often used due to their ability to capture the overall battery discharge. Un-fortunately, these simplifications lead to residual discrepancy between model predictions and observed data. In this paper, we present a hybrid modeling approach merging reduced-order models and neural networks. In this approach, while most of the input-output relationship is captured by Nernst and Butler-Volmer equations, data-driven kernels reduce the gap between predictions and observations. We validate our approach using data publicly available through the NASA Prognostics Center of Excellence repository. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations.

Lithium-ion Battery↗
NASA NTRS1993

High energy density aluminum-oxygen cell

An alternative to a secondary battery as the power source for vehicle propulsion is a fuel cell. An example of this is the metal-air fuel cell using metals such as aluminum, zinc, or iron. Aluminum is a particularly attractive candidate, having high energy and power densities, being environmentally acceptable, and having a large, established industrial base for production and distribution. An aluminum-oxygen system is currently under development for a UUV test vehicle, and recent work has focussed upon low corrosion aluminum alloys and an electrolyte management system for processing the by-products of the energy-producing reactions. This paper summarizes the progress made in both areas. Anode materials capable of providing high utilization factors over current densities ranging from S to 150 mA/sq cm have been identified. These materials are essential to realizing an acceptable mission life for the UUV. With respect to the electrolyte management system, a filter/precipitator unit has been successfully operated for over 250 hours in a large scale, half-cell system.

Rudd, E. J.↗
NASA NTRS1999

[X-33 Systems]

Lockheed Martin Skunk Works has compiled an Annual Performance Report of the X-33/RLV Program. This report consists of individual reports from all industry team members, as well as NASA team centers. This portion of the report is comprised of a status report of Allied-Signal Aerospace's contribution to the program. The following is a summary of the work reviewed under their portion of the agreement: (1) Communication Systems; (2) Environmental Control Systems- Active Thermal Control System (ATCS), Purge and Vent System, Hydrogen Detection System (HDS), Avionics Bay Inerting System (ABIS), and Flush Air Data System (FADS); (2) Landing Systems; (3) Power Management and Generation Systems; (4) Flight Control Actuation System (FCAS)- Electric Power Control & Distribution System (EPCDS), and Battery Power System (BPS); and (5) Vehicle Management Systems (VMS)- VMS Hardware, VMS Software Development Activities, and System Integration Laboratory (SIL).

Source record↗
NASA NTRS1983

Integral inverter/battery charger for use in electric vehicles

The design and test results of a thyristor based inverter/charger are discussed. A battery charger is included integral to the inverter by using a subset of the inverter power circuit components. The resulting charger provides electrical isolation between the vehicle propulsion battery and ac line and is capable of charging a 25 kWh propulsion battery in 8 hours from a 220 volt ac line. The integral charger employs the inverter commutation components at a resonant ac/dc isolated converter rated at 3.6 kW. Charger efficiency and power factor at an output power of 3.6 kW are 86% and 95% respectively. The inverter, when operated with a matching polyphase ac induction motor and nominal 132 volt propulsion battery, can provide a peak shaft power of 34 kW (45 ph) during motoring operation and 45 kW (60 hp) during regeneration. Thyristors are employed for the inverter power switching devices and are arranged in an input-commutated topology. This configuration requires only two thyristors to commutate the six main inverter thyristors. Inverter efficiency during motoring operation at motor shaft speeds above 450 rad/sec (4300 rpm) is 92-94% for output power levels above 11 KW (15 hp). The combined ac inverter/charger package weighs 47 kg (103 lbs).

Thimmesch, D.↗
NASA NTRS1977

Baseline tests of the EPC Hummingbird electric passenger vehicle

The rear-mounted internal combustion engine in a four-passenger Volkswagen Thing was replaced with an electric motor made by modifying an aircraft generator and powered by 12 heavy-duty, lead-acid battery modules. Vehicle performance tests were conducted to measure vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics. Test results are presented in tables and charts.

Slavik, R. J.↗
NASA NTRS2013

Development of a High Reliability Compact Air Independent PEMFC Power System

Autonomous Underwater Vehicles (AUV's) have received increasing attention in recent years as military and commercial users look for means to maintain a mobile and persistent presence in the undersea world. Compact, neutrally buoyant power systems are needed for both small and large vehicles. Historically, batteries have been employed in these applications, but the energy density and therefore mission duration are limited with current battery technologies. Vehicles with stored energy requirements greater than approximately 10 kWh have an alternate means to get long duration power. High efficiency Proton Exchange Membrane (PEM) fuel cell systems utilizing pure hydrogen and oxygen reactants show the potential for an order of magnitude energy density improvement over batteries as long as the subsystems are compact. One key aspect to achieving a compact and energy dense system is the design of the fuel cell balance of plant (BOP). Recent fuel cell work, initially focused on NASA applications requiring high reliability, has developed systems that can meet target power and energy densities. Passive flow through systems using ejector driven reactant (EDR) circulation have been developed to provide high reactant flow and water management within the stack, with minimal parasitic losses compared to blowers. The ejectors and recirculation loops, along with valves and other BOP instrumentation, have been incorporated within the stack end plate. In addition, components for water management and reactant conditioning have been incorporated within the stack to further minimize the BOP. These BOP systems are thermally and functionally integrated into the stack hardware and fit into the small volumes required for AUV and future NASA applications to maximize the volume available for reactants. These integrated systems provide a compact solution for the fuel cell BOP and maximize the efficiency and reliability of the system. Designs have been developed for multiple applications ranging from less than 1 kWe to 70 kWe. These systems occupy a very small portion of the overall energy system, allowing most of the system volume to be used for reactants. The fuel cell systems have been optimized to use reactants efficiently with high stack efficiency and low parasitic losses. The resulting compact, highly efficient fuel cell system provides exceptional reactant utilization and energy density. Key design variables and supporting test data are presented. Future development activities are described.

Vasquez, Arturo↗
NASA NTRSDate not supplied

Orion Power Transfer: Impacts of a Battery-on-Bus Power System Architecture

The Orion Multi-Purpose Crew Vehicle (MPCV) has the capability to transfer power to a co-manifested payload (CPL) during transit from low-Earth orbit to the lunar vicinity. This paper discusses a time-phased parametric power analysis to determine the Orion power transfer capability limit. Charge and discharge curves were generated for various power transfer conditions and measured against various minimum system voltage limits. The Orion electrical power system (EPS) utilizes an unregulated bus architecture, which has important implications when the system is operating under very high load demand conditions, such as during power transfer. This analysis highlights three important effects of this architecture. First, increasing load demand decreases the maximum state of charge (SOC) the batteries can reach when charging. Second, increasing load demand also decreases the allowable battery depth of discharge. These two together can significantly reduce the effective useable capacity of the batteries. Finally, low battery voltage decreases the power generation of the solar arrays. This can lead to significantly longer recharge times or even push the system out of energy balance. These effects have important implications for mission design and vehicle operations and must be accounted for when conducting sizing and design of an unregulated spacecraft EPS.

Electrical Power System↗