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NASA NTRS2018

The SPACE Computer Code for Analyzing the International Space Station Electrical Power System: Past, Present, and Future

The System Power Analysis for Capability Evaluation (SPACE) computer code was initially developed by NASA in 1988 to assess the Space Station Freedom electric power system and later adapted to support contractor electrical power system capability analyses for the International Space Station (ISS). Over time, the code has supported many efforts such as ISS redesign activities in the early 1990s, assessment of time-phased loads against power system operating limits for future ISS assembly flights (including Certification of Flight Readiness reviews by the ISS program office), and determining the optimum solar array gimbal positions while respecting keep-out zones which minimize both solar array contamination and structural loads. The code has been validated by comparisons with ISS on-orbit data in multiple validation episodes. Recent updates to the code include the incorporation of a Lithium-Ion battery model in addition to the Nickel Hydrogen battery model and modifications to the solar array degradation model to better match on-orbit test results. SPACE has also been extended beyond the ISS to include modeling of the Orion Multi-Purpose Crew Vehicle electrical power system (SPACE-MPCV) and Mars Surface Electrical Power Systems (MSEPS). Portions of SPACE were integrated with a trajectory code to form a Solar Electric Propulsion Simulation (SEPSim), which can be used for analyzing solar electric propulsion missions. In addition, SPACE methods and subroutines have been adapted to a multitude of other projects. This paper summarizes the initial code development and subsequent code utilization in the context of the overall ISS program development and on-orbit operations. Recent updates and results from the code are discussed, including preliminary analyses for the Orion power system.

Electrical Power System↗

NASA NTRS2018

Design and Development of Advanced Air-Cathodes for Li-Air Battery

NASA's future missions demand high energy density batteries. Current state-of-the-art lithium-ion batteries (LIBs) reach the specific energy capacity limits (<250 Wh/kg), thus new electrochemical storage technologies are needed to meet NASA's future mission requirement of >400 Wh/kg. Battery chemistries with higher theoretical energy densities in particular, Li-metal based battery chemistries such as Li-air, are being actively pursued, but there remains a number of issues to be solved. Development of advanced and optimized air cathodes will be crucial for improving capacity retention and cycle life of Li-air battery. The microstructure of carbon substrates and the catalytic oxygen reduction of the air-cathode play critical roles in the electrochemical performance in Li-air batteries. With the development and proper design of air cathodes, the energy efficiency and energy density can be enhanced significantly. In this presentation, the results of advanced permeable air cathodes design with different substrates such as 2D carbon paper or carbon cloth, vs 3D graphene or metal foam, different high surface area carbon materials and incorporation with catalysts on oxygen reduction reactions, different porosity controlling of air cathode as well the additives in electrolyte will be presented. The cycling and rate capability performance of these air-cathodes in the Li-air cells will be discussed as well.

Wu, James J.↗

NASA NTRS2018

Combining Fractional Calorimetry with Statistical Methods to Characterize Thermal Runaway

Thermal management systems designed to handle the impacts of thermal runaway (TR) are key to safe operation of lithium-ion (Li-ion) batteries. Critical factors for optimizing these systems include the total energy released and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A unique calorimeter, designed to characterize said factors, was utilized to examine the TR behavior of a variety of 18650-format Li-ion cells. Statistical methods were implemented to interpret the data.

Walker, William↗

NASA NTRS2018

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

Corbetta, M.↗

NASA NTRS2019

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

stochastic calculus↗

NASA NTRS2018

Coupling Fractional Thermal Runaway Calorimetry (FTRC) Results with Statistical Analysis Methods

Important factors to consider when optimizing Lithium-ion (Li-ion) battery thermal management systems, from a battery safety perspective, include the total energy released during a single cell thermal runaway (TR) event and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A fractional thermal runaway calorimeter (FTRC) was used to examine the TR behavior of the following 18650-format Li-ion cells: (1) Molicel 18650-J, (2) LG 18650-HG2, (3) Samsung 18650-30Q, (4) 3.35 Ah LG 18650 test cell, (5) Sony 18650-VC7, and (6) LG 18650-MJ1. These cells were intentionally selected to represent a variety of manufacturers, chemistries, capacities, and safety features. Because of the large number of random and non-random variables associated with the FTRC results, a linear regression model, developed using engineering statistical methods, was utilized to provide final interpretation of the TR behavior for each cell type and design variable combination.

Walker, William↗

NASA NTRS2015

Towards Characterizing the Variability in the Loading Demands of an Unmanned Aerial Vehicle

This paper presents a computational methodology to characterize and quantify the variability in the power demands during the take-off of an unmanned aerial vehicle (UAV). A lithium-ion battery-based power system is used to power the unmanned aerial vehicle, and the capabilities of the unmanned aerial vehicle are driven by the amount of charge in this battery. In order to design the power system, it is necessary to analyze the power and charge requirements of the UAV. This paper focuses on the take-off segment, and aims to quantify the amount of charge that is required for this particular segment. Sparse data is available through different flight tests and this data is used to analyze the flight profile and the charge requirement during take-off. The amount of charge required for take-off depends on several factors that are not only variable but cannot be controlled in reality, and hence, the entire flight profile and the corresponding charge requirement are variable in nature. The information available through flight tests is converted into multi-dimensional sparse data and a new method is developed in this paper for variability characterization using multi-dimensional sparse data. This analysis is useful for prognostics and health management where it is necessary to anticipate future charge requirements in order to compute the end-of-discharge of the battery, and hence, the remaining useful life of the power system.

unmanned aerial vehicle↗

NASA NTRS2019

Enhancing Battery Safety with Fractional Thermal Runaway Calorimetry

Effective thermal management systems, designed to handle the impacts of thermal runaway (TR) and to prevent cell-to-cell propagation, are key to safe operation of lithium-ion (Li-ion) battery assemblies. Critical factors for optimising these systems include the total energy released during a single cell TR event and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A unique fractional thermal runaway calorimeter (FTRC), designed to characterise said critical factors, was utilised to examine the TR behaviour of Li-ion cells). Delegates will be provided with an in-depth discussion on what fractional thermal runaway calorimetry is and how it benefits the development of safe and optimised battery assemblies that protect against thermal runaway. Delegates will see calorimetry data associated with a variety of Li-ion cells. Data presented is relevant to any industry that utilises high-energy Li-ion cells in closed environments and has a need to focus on safety.

Walker, William↗

NASA NTRS2020

Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

Edward F. Hogge↗

NASA NTRS2019

Characterization of Laptop Fires in Spacecraft

An accidental fire involving the Lithium-Ion (Li-ion) battery in a laptop computer is one of the most likely fire scenarios on-board a spacecraft. These fires can occur from a defect in the battery that worsens with time, over-charging the battery and leading to failure or accidental damage caused by thermal runaway. While this is a relatively likely fire scenario, very little is known about the how a laptop computer fire would impact a sealed spacecraft. The heat release would likely cause a pressure rise, possibly exceeding the pressure limit of the vehicle and causing a relief valve to open. The combustion products from the fire could pose a short-term and long-term health hazard to the crew and the fire itself could cause injury to the crew and damage to the spacecraft. Despite the hazard posed by a laptop fire, there is little quantitative data on the fire size, heat release and toxic product formation. This paper presents the results of initial attempts to quantify the fire resulting from a failed laptop fire tested at the NASA White Sands Test Facility (WSTF). The data from the testing is useful to attempt to determine the fire size and characteristics such as maximum heat release rate, total heat release, maximum temperatures and fire duration are determined. Using existing models and correlations for fires, the measured fire characteristics are extrapolated to laptop fires on a vehicle the approximate size of the Orion spacecraft.

Padilla, Rosa E.↗

NASA NTRS2019

Combining Fractional Calorimetry with Statistical Methods to Characterize Thermal Runaway

Fractional thermal runaway calorimetry (FTRC) techniques were introduced to examine thermal runaway (TR) behavior of lithium-ion (Li-ion) cells. Specifically, FTRC considers the total energy released vs. the fraction of the total energy that is released through the cell casing vs. through the ejecta material. This device has been expanded to universally support FTRC testing of additional cell types including 21700-format, D-Cell format, and large prismatic format Li-ion cells. The TR behavior as influenced by cell format, manufacturer, chemistry, capacity, and in situ safety features are described in this presentation.

Walker, William↗

NASA NTRSDate not supplied

Wind-Optimal Trajectories for Multirotor eVTOL Aircraft on UAM Missions

Distributed electric propulsion powered electric vertical takeoff and landing aircraft are expected to enable urban air mobility. Low specific energy of onboard lithium-ion polymer batteries and wind conditions impose constraints on flight endurance. Therefore, from the safety and efficiency perspective, planning and flying minimum energy trajectories are important.The primary motivation for this paper is to determine if there is an operational benefit to flying wind-optimal trajectories for short flights (less than 60 miles) in the urban environment. This study employs wind-optimal trajectories for a NASA-proposed conceptual multirotor aircraft flight in the urban environment. The optimal control model presented includes a wind model for quantifying the effect of wind on the trajectory. The optimal control problem is numerically solved using the direct method. Energy consumption and flight duration flying wind-optimal trajectories between origins and destinations in a metropolitan area are compared with energy consumption and flight duration flying on the great-circle paths between the same origin and destination pairs to determine the operational benefit of wind-optimal routing for short flights.The flight duration results for different scenarios are validated using a simulation tool designed and developed by NASA for exploring advanced air traffic management concepts. In summary,this research study suggests that for short flights in an urban environment, the wind-optimal trajectories have a very slight (insignificant) operational advantage over the great-circle trajectories in terms of energy consumption and flight duration. As expected, headwinds and tailwinds along the route of flight have a significant impact on energy consumption and flight duration.

Urban Air Mobility↗

NASA NTRSDate not supplied

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

anode↗

NASA NTRS2021

Design and Development of Nano-electro Fuel Batteries and Rim-driven Motors for Electrified Aircraft Applications

The National Aeronautics and Space Administration Convergent Aeronautics Solutions project seeks to determine feasibility of emerging technologies in aeronautics. The Aqueous, QUick-Charging Battery Integration For Electric flight Research study pursued the feasibility of nano-electro fuel and rim-driven motor technologies integrated together for aircraft implementation. Development of the NEF technology was in partnership with Influit Energy, LLC (Chicago, Illinois). A rim-driven motor, designed by the project team, sized to represent one of 24 motors in the propulsion system of the tandem electric super-short takeoff and landing aircraft is discussed. The development of this electric super-short takeoff and landing concept was in partnership with The Boeing Company (Chicago, Illinois). The integrated system design of the nano-electro fuel and rim-driven motor technologies within the wing section was in partnership with Empirical Systems Aerospace, Inc. (ESAero) (San Luis Obispo, California). The feasibility benchmark for the nano-electro fuel battery was to attain 100 mA/sq. cm within two years, a 50-fold increase over the demonstrated cell performance before the Convergent Aeronautics Solutions Aqueous, QUick-Charging Battery Integration For Electric flight Research activity. The team achieved 85 mA/sq. cm in the nano-electro fuel flow cell at the end of the two years. The Aqueous, QUick-Charging Battery Integration For Electric flight Research project team determined the technology to be both inflammable and nonexplosive (unless multiple, simultaneous system failures occur) which would provide a safer alternative to conventional lithium-ion based battery systems.

Kurt V. Papathakis↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗

NASA NTRSDate not supplied

Assessment of Cryogenic Analog Electronics for Lunar Environment

Assessment of Analog Electronics for Lunar Power Hibernation Non-nuclear robotic spacecraft on the Moon may not reliably operate past the first lunar day due to the subsequent long lunar night with an extreme low temperature range of 50K-100K. To address this concern, the NASA Glenn Research Center is developing a lunar power hibernation protocol that could enable spacecraft to passively survive the lunar night without any additional heating source and then reactivate at lunar dawn. Low cost lunar robotic missions could extend their mission lifetime from a single lunar cycle to potentially many months, thus greatly extending science data gathering and return on investment for commercial missions. This approach centers on evidence that lithium-ion battery cells can recover to full operation after being subjected to cryogenic temperatures. Exploiting this battery capability requires that all spacecraft electronics, including power system and avionics, passively tolerate the extreme cold without degradation. Key elements in the power system will also need to be capable of actively operating in the extreme cold to perform the functions required to restore the system to full operation after solar power returns at lunar dawn. This presentation provides an assessment of extreme-cold tolerance and operational capability of analog electronics where choices in semiconductor material, device type, and circuit fabrication technology has a dramatic impact on circuit behavior and reliability at low temperatures.

Lunar↗

NASA NTRS2021

Thermal Systems Modeling of Chemical Integrated Power Source (CHIPS) to Survive Lunar Night Environments

This paper presents the results of the systems level thermal modeling for a conceptual Chemical Heat Integrated Power Source (CHIPS). This proposed system offers a combined thermal and electrical power source to support survival of spacecraft operating in extreme low temperature lunar environments, without the use of radioisotope-based sources. A conceptual design study has been completed for this system, that uses heat generated by an exothermic chemical reaction in place of radioisotope or electrical heat sources. The goal of the study was to evaluate the feasibility of such as system through thermodynamic and chemical analysis and thermal modeling, and to identify technology gaps to inform a technology development and maturation plan. The specific technical objectives focused on delivery of 90-100 Wth thermal power and 30-40 We electrical power for 336 hours to a representative Commercial Lunar Payload Services (CLPS) lander, to support lunar surface survival and limited operations through a lunar night. The total system mass was targeted at ≤50 kg. A highly exothermic chemical reaction system is used to generate on-board electrical power for spacecraft systems, and thermal power to maintain critical spacecraft/lander systems within their allowable flight temperature (AFTs). Based on the very high energy content of the chemical reaction system, a much higher amount of heat per unit mass can be delivered to the spacecraft, relative to a rechargeable lithium-ion battery and electrical heater(s). Since radioisotope heaters or generators are not used, the system will be orders of magnitude lower in cost than a radioisotope heating/power unit, without the attendant regulatory complexities. As part of the CHIPS concept, a fraction of the thermal power generated is converted to electrical power via an appropriate thermal-to-electric converter technology (such as a free piston Stirling converter or a thermoelectric generator module), to provide power to critical loads. This approach is ideally suited to support operation of commercial landers (e.g., via the CLPS program). In most cases, these landers are only designed to operate during a portion of the lunar day, with no provision for survival through the lunar night. By supporting lunar night survival, a mission can be extended through the lunar night and at least into another lunar day, thus turning a nominal eight-day mission into a 36-day mission. Therefore, the current system demonstration will focus on scalability to support at least 336 hours (one lunar night) of continuous thermal and electrical power generation. Although initially targeted to support lunar equatorial landings, the technology is extensible to missions at the lunar poles, other extreme environments in the Solar System or even air-independent applications on Earth (e.g., ocean exploration).

West, William↗