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NASA NTRSDate not supplied

Development and Application of a Novel Calorimetry Technique for the Study of Lithium-Ion Cell Thermal Runaway

Lithium-ion battery technology is widely used and is attractive due to demonstrated specific energies in the 200-300 W-hr/kg range. The excellent, mass-efficient energy storage capability of lithium-ion batteries has led to their use on many aerospace platforms. However, lithium-ion batteries can exhibit thermal runaway behavior wherein stored electrochemical energy is released rapidly as a result of thermal or mechanical failure, electrochemical abuse, internal or external short circuiting. A single cell undergoing thermal runaway within a battery has the potential to induce thermal runaway in adjacent cells if heat dissipation is not properly managed and can result in a catastrophic failure of the battery. Designing batteries that are resistant to thermal runaway propagation requires an understanding of, not only, total energy yield but also the means by which that energy is liberated from the cell. While Accelerating Rate Calorimetry and other techniques provide total thermal runaway energy yield, they do not provide the fractional breakdown of energy liberated via conduction through the cell casing from that which is vented from the cell as hot gases and effluents. Such data are needed to inform battery thermal design and analysis. To measure the total energy yield, the fraction conducted through the cell casing, and the fraction lost due to gases and effluents, NASA developed Fractional Thermal Runaway Calorimetry (FTRC). Two calorimeters have been developed and demonstrated, the Small-format- and Large-format Fractional Thermal Runaway Calorimeters (S-FTRC and L-FTRC, respectively). The technique has been successfully applied to small- and large-format cells (2.4-3.5 Ah and >100 Ah capacity, respectively) and has given new insights into Li-ion cell thermal runaway. Development of the calorimeters is discussed and results from the initial thermal runaway testing campaigns are presented.

Lithium-Ion Cell↗

NASA NTRS2017

A Novel UAV Electric Propulsion Testbed for Diagnostics and Prognostics

This paper presents a novel hardware-in-the-loop (HIL) testbed for systems level diagnostics and prognostics of an electric propulsion system used in UAVs (unmanned aerial vehicle). Referencing the all electric, Edge 540T aircraft used in science and research by NASA Langley Flight Research Center, the HIL testbed includes an identical propulsion system, consisting of motors, speed controllers and batteries. Isolated under a controlled laboratory environment, the propulsion system has been instrumented for advanced diagnostics and prognostics. To produce flight like loading on the system a slave motor is coupled to the motor under test (MUT) and provides variable mechanical resistance, and the capability of introducing nondestructive mechanical wear-like frictional loads on the system. This testbed enables the verification of mathematical models of each component of the propulsion system, the repeatable generation of flight-like loads on the system for fault analysis, test-to-failure scenarios, and the development of advanced system level diagnostics and prognostics methods. The capabilities of the testbed are extended through the integration of a LabVIEW-based client for the Live Virtual Constructive Distributed Environment (LVCDC) Gateway which enables both the publishing of generated data for remotely located observers and prognosers and the synchronization the testbed propulsion system with vehicles in the air. The developed HIL testbed gives researchers easy access to a scientifically relevant portion of the aircraft without the overhead and dangers encountered during actual flight.

Gorospe, George E., Jr.↗

NASA NTRS2013

Are Soft Short Tests Good Indicators of Internal Li-ion Cell Defects?

The self discharge test at full state of charge, may not be a good one to detect subtle defects since the li-ion chemistry has the highest self discharge at full state of charge. One should characterize self discharge versus storage time for each cell manufacturer/design to differentiate between normal self discharge and that due to a subtle manufacturing defect. The various soft short test methods indicate that if this test is carried out at full discharge (0% SOC) with all capacity removed (by lowering the current load in a stepwise manner to the same end of discharge voltage), then the cells need to be placed in storage for more than 72 hours to get a good analysis on the presence of subtle defects since it takes more than 72 hours to achieve voltage stabilization. If the cells are to be charged up even to a small percentage (ex. 1%), 72 hours are sufficient to determine issues. However, the pass/fail criteria should be based on a valid OCV decline. Less than 10 mV voltage decline is not a good method to detect subtle defects. As mentioned in the first bullet, self discharge is a competing reaction when a charge is introduced and hence a characterization of the self discharge versus storage time is required to fully correlate voltage decline to a failure due to a subtle defect. Soft short test method cannot be relied on for defect detection because cells with and without voltage decline seemed to have similar defects and characteristics. Screening methods such as internal resistance and capacity as well as a 3-sigma range for OCV, mass and dimensions should be used to screen out outliers. A very critical aspect in the understanding of subtle defects is to carry out destructive analysis of cells from every lot to confirm the quality of production and screen all cells and batteries in a stringent manner to have a high quality set of flight cells. Self Discharge Test: Fully charged cells shall be placed in Open circuit stand for 72 hours (OCV measurement twice a day); continue for total of 14 days with 1 reading per day 2. Soft Short Test 1: Fully charge; cells discharged to manufacturer's end of disch. Voltage (EODV) cutoff at C/5 rate; stand for 30 minutes; discharge with C/500 to the same EODV. stand for another 30 minutes; discharge the cells again using C/1000 current to the same EODV. OCV measurements twice a day for 72 hours and then for total of 14 days (data collection same as in 1.) 3. Soft Short Test 2: Fully charge; cells discharged to the manuf. EODV with a C/13 constant current; provide a 10 hour rest, discharge again to the same EODV with a current of C/250, provide a 10 hour rest, discharge again using a C/250 rate, provide a 24 hour rest, charge using C/250 to 3.15 V (for ~12 hours). OCV measurements twice a day for at 72 hours. (data collection same as in 1.) 4. Soft Short Test 3: Fully charge; cells shall be discharged using C/10 current to manuf. EODV. Allow the cell to remain at Open circuit for 10 seconds. Discharge the cell at C/20 rate to the same EODV, hold open circuit for 24 hours. Discharge the cells at C/200 rate to the same end of voltage cutoff and hold open circuit for 24 hours. Discharge the cells one more time at C/200 rate to the same EODV and hold open circuit for 36 hours. Charge at C/200 rate to 3.15 V and hold for 3 days. Record OCV during the open circuit stand periods every 12 hours and at the beginning and end of the 3 day hold (include the 12 hour OCV recording during this time also). Capacity Cycling: Cells with declining voltages - one cell from each manufacturer chosen for cycling Destructive Physical Analysis (DPA): Cells with and without decline chosen from each lot for DPA.

Jeevarajan, J.↗

NASA NTRSDate not supplied

In-Time Safety Assessment & Risk Prediction for Unmanned Aerial Systems

One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.

diagnostics↗

NASA NTRS1990

Modelling a nickel-cadmium battery as a heterogeneous device

A common failure mode for nickel-cadmium batteries involves a nonuniform degradation of the performance of individual cells. This behavior is manifested as a divergence in voltage during cyclic operation. An empirically based performance model is presented which simulates a battery composed of heterogeneous cells. Battery performance is studied as a function of the distribution of cell performance characteristics. The technique has successfully predicted cell voltage divergence behavior observed during cycle life testing. It is noted that this work enhances understanding of life-test results and may improve life-test design and data analysis.

Glueck, Peter R.↗

NASA NTRS1999

Nondestructive Evaluation Correlated with Finite Element Analysis

Advanced materials are being developed for use in high-temperature gas turbine applications. For these new materials to be fully utilized, their deformation properties, their nondestructive evaluation (NDE) quality and material durability, and their creep and fatigue fracture characteristics need to be determined by suitable experiments. The experimental findings must be analyzed, characterized, modeled and translated into constitutive equations for stress analysis and life prediction. Only when these ingredients - together with the appropriate computational tools - are available, can durability analysis be performed in the design stage, long before the component is built. One of the many structural components being evaluated by the NDE group at the NASA Lewis Research Center is the flywheel system. It is being considered as an energy storage device for advanced space vehicles. Such devices offer advantages over electrochemical batteries in situations demanding high power delivery and high energy storage per unit weight. In addition, flywheels have potentially higher efficiency and longer lifetimes with proper motor-generator and rotor design. Flywheels made of fiber-reinforced polymer composite material show great promise for energy applications because of the high energy and power densities that they can achieve along with a burst failure mode that is relatively benign in comparison to those of flywheels made of metallic materials Therefore, to help improve durability and reduce structural uncertainties, we are developing a comprehensive analytical approach to predict the reliability and life of these components under these harsh loading conditions. The combination of NDE and two- and three-dimensional finite element analyses (e.g., stress analyses and fracture mechanics) is expected to set a standardized procedure to accurately assess the applicability of using various composite materials to design a suitable rotor/flywheel assembly.

Abdul-Azid, Ali↗

NASA NTRSDate not supplied

Material Property Estimation in Thin Battery Components Using Guided Wave Measurement, Experimental Dispersion Curve Extraction and Finite Element Modeling

At NASA, we are investigating nondestructive evaluation (NDE) and structural health monitoring (SHM) techniques to detect precursors of thermal runaway failure in lithium metal based lithium ion batteries (LIB). The approach is centered on computational simulation models to guide inspection and aid in interpretation of results. Since lithium metal LIBs have combinations of solid and fluid-filled porous materials, obtaining accurate material properties is both challenging and critical for successful simulation of battery inspection. To this end, we investigated a multi-verification approach for material characterization of thin battery components. First, a laser Doppler vibrometer (LDV) was used to measure guided wave fields in thin (microns-thick) battery components subject to broadband excitation. The time-space wavefield data was converted to frequency-wave number data to extract guided wave dispersion curves. A data visualization and post processing graphics user interface (GUI) was developed at NASA to aid the data exploration and analysis. Due to the thinness of the samples, low frequency-thickness-product plate wave approximations allowed for the calculation of closed-form solutions for material elastic property estimation. These approximations were then verified by calculating the Lamb wave solutions using the previously obtained material properties. Finally, the estimated elastic material properties were implemented in a COMSOL simulation model, and dispersion curves were extracted from simulation results. The dispersion curves and derived material properties were then compared to the analysis results from the experimental data. These comparisons informed on the accuracy of the measured material properties and helped demonstrate the accuracy of the finite element analysis (FEA) computational models. This assessment will prove vital when we start simulating more complex multi-layer components and poroelastic media. This paper gives a brief background of the problem space, outlines the workflow for data analysis and verification, shows results from the workflow, and gives an overview of future plans for simulation of lithium metal LIB inspection.

Peter Juarez↗

NASA NTRS2005

A multiparameter wearable physiologic monitoring system for space and terrestrial applications

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.

NASA Center ARC↗

NASA NTRS1992

Project Ares 3

The mission of Project Ares is to design and fabricate an Earth prototype, autonomous flying rover capable of flying on the Martian surface. The project was awarded to California State University, Northridge (CSUN) in 1989 where an in-depth paper study was completed. The second year's group, Project Ares 2, designed and fabricated a full-scale flight demonstration aircraft. Project Ares 3, the third and final group, is responsible for propulsion system design and installation, controls and instrumentation, and high altitude testing. The propulsion system consists of a motor and its power supply, geartrain, and propeller. The motor is a four-brush DC motor powered by a 50-V NiCd battery supply. A pulley and belt arrangement is used for the geartrain and includes light weight, low temperature materials. The propeller is constructed from composite materials which ensures high strength and light weight, and is specifically developed to provide thrust at extremely high altitudes. The aircraft is controlled with a ground-based radio control system and an autopilot which will activate in the event that the control signal is lost. A transponder is used to maintain radar contact for ground tracking purposes. The aircraft possesses a small, onboard computer for collecting and storing flight data. To safeguard the possibility of computer failure, all flight data is transmitted to a ground station via a telemetry system. An initial, unpowered, low-level test flight was completed in August of 1991. Testing of systems integration in the second low-level test flight resulted in loss of elevator control which caused considerable damage on landing. Complete failure analysis and repairs are scheduled for September of 1992.

Raymer, Dan↗

NASA NTRS2016

NASA Alternative Orion Small Cell Battery Design Support

The NASA Orion Crew Module Reference Design was produced to address large scale thermal runaway (TR) hazard with specific safety controls for the Orion Spacecraft. The design presented provides the description of a full scale battery design reference for implementation as a drop in replacement to meet all spacecraft energy requirements with compatible 120 Vdc electrical and mechanical interface using small cell technology (18650) packaging. The 32V SuperBrick incorporates unique support features and an electrical bus bar arrangement that allows cells negative can insertion into heat sink that is compressively coupled to the battery enclosure to promote good thermal management. The housing design also provides an internal flame suppression "filter tray" and positive venting path internal to the enclosure to allow hot effluent ejecta to escape in the event of single cell TR. Virtual cells (14P Banks) that are supported to provide cell spacing with interstitial materials to prevent side can failures that can produce cell to cell TR propagation. These features were successfully test in four separate TR run with the full scale DTA1 test article in February 2016. Successfully Completed Test Objectives - Four separate TR test runs with Full-Scale DTA1 housing with Two SuperBricks, Two SuperBrick Emulators All Tests resulted in "clean" gas with less than 6 C rise at Battery vent All Tests resulted in less than 2 C temperature rise on cold-plate outlet All Tests resulted in less than 6 psi pressure rise in the battery housing Test Run 1 -One neighbor cell TR, highest remaining neighbor 139 C. Ejecta shorted to bus caused prolonged additional heating, One shorted cell did experience TR after 12 minutes, remaining cells had adequate thermal margin Test Run 2 - No cell to cell propagation, highest neighbor cell 112 C; Test Run 3 - No cell to cell propagation, highest neighbor cell 96 C; Test Run 4 - No cell to cell propagation, highest neighbor cell 101 C; Primary TR testing and analysis were completed and reviewed for endorsement by NASA Engineering and Safety Center team members. All Key Test Objectives were met and the small cell design alternative was demonstrated and selected to be a feasible drop in replacement for the MPCV Orion CM Battery for EM2 mission.

Haynes, Chuck↗

NASA NTRS2011

EMU Lessons Learned Database

As manned space exploration takes on the task of traveling beyond low Earth orbit, many problems arise that must be solved in order to make the journey possible. One major task is protecting humans from the harsh space environment. The current method of protecting astronauts during Extravehicular Activity (EVA) is through use of the specially designed Extravehicular Mobility Unit (EMU). As more rigorous EVA conditions need to be endured at new destinations, the suit will need to be tailored and improved in order to accommodate the astronaut. The Objective behind the EMU Lessons Learned Database(LLD) is to be able to create a tool which will assist in the development of next-generation EMUs, along with maintenance and improvement of the current EMU, by compiling data from Failure Investigation and Analysis Reports (FIARs) which have information on past suit failures. FIARs use a system of codes that give more information on the aspects of the failure, but if one is unfamiliar with the EMU they will be unable to decipher the information. A goal of the EMU LLD is to not only compile the information, but to present it in a user-friendly, organized, searchable database accessible to all familiarity levels with the EMU; both newcomers and veterans alike. The EMU LLD originally started as an Excel database, which allowed easy navigation and analysis of the data through pivot charts. Creating an entry requires access to the Problem Reporting And Corrective Action database (PRACA), which contains the original FIAR data for all hardware. FIAR data are then transferred to, defined, and formatted in the LLD. Work is being done to create a web-based version of the LLD in order to increase accessibility to all of Johnson Space Center (JSC), which includes converting entries from Excel to the HTML format. FIARs related to the EMU have been completed in the Excel version, and now focus has shifted to expanding FIAR data in the LLD to include EVA tools and support hardware such as the Pistol Grip Tool (PGT) and the Battery Charger Module (BCM), while adding any recently closed EMU-related FIARs.

Matthews, Kevin M., Jr.↗

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 NTRS2010

TERRA Battery Thermal Control Anomaly - Simulation and Corrective Actions

The TERRA spacecraft was launched in December 1999 from Vandenberg Air Force Base, becoming the flagship of NASA's Earth Observing System program to gather data on how the planet's processes create climate. Originally planned as a 5 year mission, it still provides valuable science data after nearly 10 years on orbit. On October 13th, 2009 at 16:23z following a routine inclination maneuver, TERRA experienced a battery cell failure and a simultaneous failure of several battery heater control circuits used to maintain cell temperatures and gradients within the battery. With several cells nearing the minimum survival temperature, preventing the electrolyte from freezing was the first priority. After several reset attempts and power cycling of the control electronics failed to reestablish control authority on the primary side of the controller, it was switched to the redundant side, but anomalous performance again prevented full heater control of the battery cells. As the investigation into the cause of the anomaly and corrective action continued, a battery thermal model was developed to be used in determining the control ability remaining and to simulate and assess corrective actions. Although no thermal model or detailed reference data of the battery was available, sufficient information was found to allow a simplified model to be constructed, correlated against pre-anomaly telemetry, and used to simulate the thermal behavior at several points after the anomaly. It was then used to simulate subsequent corrective actions to assess their impact on cell temperatures. This paper describes the rapid development of this thermal model, including correlation to flight data before and after the anomaly., along with a comparative assessment of the analysis results used to interpret the telemetry to determine the extent of damage to the thermal control hardware, with near-term corrective actions and long-term operations plan to overcome the anomaly.

Grob, Eric W.↗

NASA NTRS2019

Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk analysis for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating over the intervals of time within which a fault occurs, accounting for an infinite number of cases. The response of the spacecraft to different faults can result in different schedules of activities, changing the intervals of integration. Europa currently uses models of spacecraft systems and components to simulate individual flight scenarios. The goal is to develop a framework for integrating, automating, and improving this modeling process. We describe an approach to generating the schedules for the different fault cases and determining the intervals for faults. It is not enough to just simulate individual cases because we are working with continuous variables that generate an infinite number of possible futures. Instead, we determine time windows within which certain faults can occur and use these time windows as bounds for integration. We found that determining these time windows is a constraint optimization problem. In order to represent these problems, we employ a language based on ontologies of behavior and scenarios. The language enables us to specify constraints in a simple, declarative syntax. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. For example, we created a detailed model of power generation, power use, and the corresponding effects on the battery in order to determine when an undervoltage fault can occur. An undervoltage during a trajectory correction maneuver requires that thrusting be interrupted for just enough time to recharge the battery such that the maneuver can be completed within battery limits. This behavior is generated based on the model to minimize the interruption time. For certain scenarios the constraint optimization problems were simple enough to be solved by hand, but the framework made the process substantially faster. It also produced solutions to other problems that we could not solve by hand or with existing tools and allowed us to generate and run many scenarios at once. The scenario language and engine greatly simplified the process of identifying time bounds and separating cases.

Everline, Chester J.↗