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NASA NTRS2014

A Physics-Based Modeling Framework for Prognostic Studies

Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles.

Li-ion Batteries↗

NASA NTRS2015

Oxychlorine Species on Mars: The Gale Crater Story

Comparing data from the Alpha- Particle X-Ray Spectrometer (APXS) and the Sample Analysis at Mars (SAM) instruments on MSL reveals a strong linear correlation between chlorine and oxygen, further demonstrating the presence of oxychlorine species in Gale Crater and, very likely, globally on Mars. Perchlorate was first discovered on Mars by the Microscopy, Electrochemistry, and Conductivity Analyzer (MECA) instrument on the Phoenix lander in 2008. Current hypotheses suggest that the formation of oxychlorine species such as perchlorate or chlorate is a global process and that these species should be globally distributed on Mars [e.g. 2-4]. To date, the SAM and Chemistry and Mineralogy (CheMin) instruments on MSL have analyzed one scooped sample of aeolian material (Rocknest [RN]), and four drilled samples (John Klein [JK], Cumberland [CB], Windjana [WJ], and Confidence Hills [CH]). The APXS instrument has also investigated the same or very similar samples. Although not definitively identified, oxychlorine species have been proposed to explain releases of O2, HCl, and chlorinated hydrocarbon species detected by evolved gas analysis (EGA) with the SAM instrument. We report a strong linear correlation between wt. % Cl detected by APXS and moles O2 detected by SAM during pyrolysis, indicating the presence of oxychlorine species in Gale Crater.

Archer, P. D., Jr.↗

NASA NTRS2009

The Phoenix Mars Lander Robotic Arm

The Phoenix Mars Lander Robotic Arm (RA) has operated for over 150 sols since the Lander touched down on the north polar region of Mars on May 25, 2008. During its mission it has dug numerous trenches in the Martian regolith, acquired samples of Martian dry and icy soil, and delivered them to the Thermal Evolved Gas Analyzer (TEGA) and the Microscopy, Electrochemistry, and Conductivity Analyzer (MECA). The RA inserted the Thermal and Electrical Conductivity Probe (TECP) into the Martian regolith and positioned it at various heights above the surface for relative humidity measurements. The RA was used to point the Robotic Arm Camera to take images of the surface, trenches, samples within the scoop, and other objects of scientific interest within its workspace. Data from the RA sensors during trenching, scraping, and trench cave-in experiments have been used to infer mechanical properties of the Martian soil. This paper describes the design and operations of the RA as a critical component of the Phoenix Mars Lander necessary to achieve the scientific goals of the mission.

Bonitz, Robert↗

NASA NTRS2017

Electrochemical Detection of the Molecules of Life

All forms of life on Earth contain cellular machinery that can transform and regulate chemical energy through metabolic pathways. These processes are oxidation-reduction reactions that are performed by four key classes of molecules: flavins, nicotinamaides, porphyrins, and quinones. By detecting the electrochemical interaction of these redox-active molecules with an electrode, a method of differentiating them by their class could be established and incorporated into future life-detecting missions. This body of work investigates the electrochemistry of ubiquitous molecules found in life and how they may be detected. Molecules can oxidise or reduce the surface of an electrode - giving or receiving electrons - and these interactions are represented by changes in current with respect to an applied voltage. This relationship varies with: electrolyte type and concentration, working electrode material, the redox-active molecule itself, and scan rate. Flavin adenine dinucleotide (FAD), riboflavin, nicotinamide adenine dinucleotide (NADH), and anthraquinone are all molecules found intracellularly in almost all living organisms. An organism-synthesised extracellular redox-active molecule, Plumbagin, was also selected as part of this study. The goal of this work is to detect these molecules in seawater and assess its application in searching for life on Ocean Worlds.

astrobiolog↗

NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗

NASA NTRS2017

Safe, High Specific Energy & Power Lithium Ion Cell Designs

The higher energy content (265 Wh/kg, 725 Wh/L) of the newer cell designs from LG, Panasonic, and Samsung have made them susceptible to side wall ruptures (SWR) during thermal runaway, rather than venting through the intended vent path in the cell header. This is also due to higher reaction kinetics of the electrochemistry, thinner can walls, tight crimp enclosure of the cell header, and inadequate flow rate through the header vent. This effort determined that bottom vents and thicker cell can walls both are necessary for reducing the risk of SWR. Compelling evidence was obtained using novel test methods that included an on-demand internal short circuit device, high speed X-ray videography, and cell thermal runaway calorimetry.

side wall rupture↗

NASA NTRS2017

Multifunctional Structures for High-Energy Lightweight Load-Bearing Storage

The Multifunctional Structures for High Energy Lightweight Load-bearing Storage (M-SHELLS) subproject is evaluating the feasibility of a structural hybrid super-capacitor concept, drawn from recent nano-technology advances in both electrochemistry and microstructures to create a transformative multi-functional airframe material which carries structural load and stores energy to save weight and enable electric aero propulsion. Multi-disciplinary engineering approach assures high energy storage and power levels, as well as good mechanical attributes.

Loyselle, Patricia↗

NASA NTRSDate not supplied

Computational Approaches for Li-O2 Battery Design

Threshold energy densities for general aviation electric aircraft are 400 Wh/kg with more ambitious air vehicles having significantly higher requirements. Li-O2 batteries, with the highest theoretical capacity, are one of the few “beyond Li-ion” chemistries that might satisfy the extraordinary specific capacity as well as specific power requirements of electric aircraft. However, side reactions at interfaces, in particular at the cathode, over the charge-discharge cycles result in very short cycle-life and dramatic reduction of capacity. Addressing these issues, in addition to others, are crucial for realizing practical, high performance Li-O2 batteries. In this talk, we discuss atomistic computational work to understand and mitigate some of the issues, including those at interfaces, that affect the Li-O2 electrochemistry. To start, we discuss the deposition mechanisms, both surface and solution based, of Li2O2 and their dependence on external potential. Next, we explore molten salt electrolytes as a stable alternative to organic electrolytes and approaches taken to develop new practical molten salt eutectic mixtures. Finally, we address the issue of reactive carbon-cathodes and possible cathode-candidates that were identified throughput high-throughput computations.

Li-Air battery↗

NASA NTRS2020

Toward Realizing the Potential of Practical Li-O2 Batteries for Electric Aircraft

Electrochemical power sources based on the Li-O2 couple have potential to achieve more than two-times the specific energy that is achievable with state-of-the-art Li-ion technologies. Despite considerable research, significant practical limitations continue to impede the successful realization of the Li-O2 electrochemistry as a power source in electric and hybrid-electric aviation. In practice, power capability and cycle life present formidable challenges. In this work, projections are presented for Li-O2prismatic cells with lightweight construction and the effect of cathode-limited capacity and discharge rate on specific energy and power are presented.

William R Bennett↗

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 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

LIRA: Probing the Lunar Surface for Resources.

The thermal, compositional and electrical properties of the lunar regolith, such as the triboelectric properties of lunar dust, the volatile content and the thermophysical properties of the polar regolith are essential to understand from a scientific perspective as well as for resource extraction and hazard mitigation. Understanding these properties specifically requires in situ measurement, as these properties are inherent in the ambient environment. With the Lunar In situ Regolith Analyzer (LIRA) we will combine mature surface sensor technologies from previous spacecraft missions (TECP) with new sensors (ClO4- ) and battery capability to achieve a compact in situ analysis package. The surface instrument suite is adapted for lunar conditions from a sensor developed as part of the Microscopy, Electrochemistry, and Conductivity Analyzer (MECA) instrument on the 2007 Mars Phoenix mission, as well as supplemental sensors. • The Thermal and Electrical Conductivity Probe (TECP), flown on MECA/Phoenix, which measures heat capacity, thermal conductivity, electrical resistance, and electrical permittivity between needles inserted into the soil. The TECP also measured atmospheric humidity, a capability that will be retained to search for subsurface H2O ice. The Mars TECP weighed 100-gram and by definition was at TRL 9. The lunar version (LIRA) is TRL 4. • The Perchlorate (ClO4) sensor is based on the fluorescence property of perchlorate (Mg, Fe, Na). We added a light source to excite the perchlorate (ClO4) and a photo diode to collect the emission wavelength. A sun-shade over the photo diode blocks out the ambient light during day light hours. The unifying goal of LIRA is to deliver a complete, integrated experiment suite that investigates properties that must be measured in situ, and provides the capability to power the suite at night or in PSRs, as well as a means to simulate aspects of the diurnal transition by delivering an optical pulse to the surface with intensity comparable to sunlight. We describe the scientific context of LIRA, touching on the electrical state of the near-surface environment, the thermal properties of the undisturbed regolith, and the distribution, geochemistry, and mobility of water. We also address the exploration implications of the work, examining hazards and resources in the context of strategic knowledge gaps (SKGs).

Heather Smith↗

NASA NTRSDate not supplied

Predicting Maximum Temperatures of a Li-ion Battery on a Simulated Flight Profile using a Model-based Prognostics

One of the challenges in using Li-ion packs in aeronautics is their safety, and thermal runaway (TR) is a major concern. The current engineering solutions to prevent a Li-ion pack from a catastrophic TR require additional mass and volume to isolate cells. The excess mass could be reduced by improving detection and, thus, preventing a TR event. One of the possible early warning indicators of a TR is crossing a threshold temperature. We have developed an approach, based on the Unscented Kalman Filter (UKF), to predict the likelihood of reaching the threshold temperature for simulated flight profiles. The current battery prognostics algorithms for aerospace predict state-of-charge (SOC) and end-of-life (EOL) [1]. We extended this two-level algorithm to predict the maximum temperature during discharge. The amount of heat generated in a cell depends on factors such as cell chemistry, cell packaging, total cycles, operating temperature, and abuse history [2]. Our semi-empirical thermal model depends on three phenomenological parameters which account for those factors. In addition, a two-parameter reduced-order model is developed to predict the temperature rise for short bursts of “random-walk” (RW) discharge current sequence, which simulates a flight's current-loading profile. The performance of these models on different datasets and types of current loading will be presented. To predict the maximal temperatures for future cycles we must estimate the evolution of thermal parameters as the batteries age. It is found that the parameters of the 3-parametric thermal model cannot be estimated only from the RW data. To address the issue, we will present two alternative approaches: i) expanding the datasets to include discharge profiles beyond RWs; ii) model reduction to a two-parametric model. The two approaches will be illustrated by an application to the cycling data from a commercial LG 18650 cell. References: 1. M. Daigle, C.S. Kulkarni, End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models, in: AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics, San Diego, California, USA, 2016. 2. M. Börner, et. al, Correlation of aging and thermal stability of commercial 18650-type lithium ion batteries, Journal of Power Sources. 342 (2017) 382–392.

Thermal runaway↗

NASA NTRSDate not supplied

Silver Electrolysis for Disinfection of Spacecraft Potable Water: 2023 Update

Anodic dissolution of silver electrodes, or “silver electrolysis,” is being investigated as a means of introducing biocidal silver into potable water on exploration spacecraft. Last year’s paper reported a fault condition termed “electrode bridging,” in which silver deposits on interelectrode wetted materials, eventually bridging the electrode gap and providing an alternate conduction pathway that diminishes the silver output of the reactor. Since then, two strategies have been developed that prevent this fault from occurring: 1) increasing the frequency of polarity reversal and 2) separating the active surface of the electrode from the interelectrode wetted material. These strategies were tested independently, and in both cases, the reactor performed as expected with no occurrence of bridging for at least one crew-year equivalent of cumulative operation. Additional tests were conducted at varying levels of influent dissolved oxygen to better understand the nature of the cathode reaction and its impact on reactor performance. Results indicate that the primary cathode reaction is the reduction of dissolved oxygen, which could imply a dependence of reactor performance on dissolved oxygen concentration. However, it was found that at the typical oxygen concentrations anticipated for spacecraft processed water, the impact on silver electrolysis performance was negligible. Finally, multiphysics modelling of the reactor fluid mechanics and electrochemistry was undertaken to provide the capability to optimize the design of a future reactor prototype.

silver electrolysis↗

NASA NTRS2023

Long Life Molten Salt Battery for NASA Venus Application

A development program is being conducted at Advanced Thermal Batteries (ATB) to create a low discharge rate, long life, molten salt battery for a NASA Venus surface probe. Battery goals are 60 days continuous operation at +25V +0/-6 volts and -25V +6/-0 Volts under severe environments of 465°C, 92 atm pressure, and corrosive sulfur dioxide in a super critical carbon dioxide atmosphere. Conveniently, molten salt thermal battery electrochemistry starts to operate efficiently at 330°C. However, a major barrier to direct application of existing thermal battery technology is the high self-discharge rate, resulting in lifetimes in only minutes, not days. The best performance to date, which meets the required voltage regulation, has been a lithium silicon (LiSi) alloy anode discharging through all three voltage plateau versus an iron monosulfide (FeS) cathode. In order to perform reproducibly, additional engineering controls within the battery design are required to limit cell–to–cell parasitic discharge mechanisms. Results of these trials have improved the efficiency and manufacturability of the design.

Thermal Battery↗

NASA NTRSDate not supplied

Silver Electrolysis for Disinfection of Spacecraft Potable Water: 2023 Update

silver electrolysis↗

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 NTRS2022

Electroactive ZnO: Mechanisms, Conductivity, and Advances in Zn Alkaline Battery Cycling

Zinc oxide is of great interest for advanced energy devices because of its low cost, wide direct bandgap, non-toxicity, and facile electrochemistry. In zinc alkaline batteries, ZnO plays a critical role in electrode passivation, a process that hinders commercialization and remains poorly understood. Here, novel observations of an electroactive type of ZnO formed in Zn-metal alkaline electrodes are disclosed. The electrical conductivity of battery-formed ZnO is measured and found to vary by factors of up to 104, which provides a first-principles-based understanding of Zn passivation in industrial alkaline batteries. Simultaneous with this conductivity change, protons are inserted into the crystal structure and electrons are inserted into the conduction band in quantities up to ≈1020 cm−3 and ≈1 mAh gZnO−1. Electron insertion causes blue electrochromic coloration with efficiencies and rates competitive with leading electrochromic materials. The electroactivity of ZnO is evidently enabled by rapid crystal growth, which forms defects that complex with inserted cations, charge-balanced by the increase of conduction band electrons. This property distinguishes electroactive ZnO from inactive classical ZnO. Knowledge of this phenomenon is applied to improve cycling performance of industrial-design electrodes at 50% zinc utilization and the authors propose other uses for ZnO such as electrochromic devices.

Brendan E. Hawkins↗