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Results for “end of discharge”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

End-of-Discharge and End-of-Life Prediction in Lithium-Ion Batteries with Electrochemistry-Based Aging Models

As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (i) accurately predict end-of-discharge for aged batteries, and (ii) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.

batteries↗
NASA NTRS1999

System for Manipulating Drops and Bubbles Using Acoustic Radiation Pressure

The manipulation and control of drops of liquid and gas bubbles is achieved using high intensity acoustics in the form of and/or acoustic radiation pressure and acoustic streaming. generated by a controlled wave emission from a transducer. Acoustic radiation pressure is used to deploy or dispense drops into a liquid or a gas or bubbles into a liquid at zero or near zero velocity from the discharge end of a needle such as a syringe needle. Acoustic streaming is useful in manipulating the drop or bubble during or after deployment. Deployment and discharge is achieved by focusing the acoustic radiation pressure on the discharge end of the needle, and passing the acoustic waves through the fluid in the needle. through the needle will itself, or coaxially through the fluid medium surrounding the needle. Alternatively, the acoustic waves can be counter-deployed by focusing on the discharge end of the needle from a transducer axially aligned with the needle, but at a position opposite the needle, to prevent premature deployment of the drop or bubble. The acoustic radiation pressure can also be used for detecting the presence or absence of a drop or a bubble at the tip of a needle or for sensing various physical characteristics of the drop or bubble such as size or density.

Oeftering, Richard C.↗
NASA NTRS2002

Hypergolic Ignitor Assembly

An ignitor for use with the MC-I rocket engine has a cartridge bounded by two end caps with rupture disc assemblies connected thereto. A piston assembly within the cartridge moves from one end of the cartridge during the ignition process. The inlet of the ignitor communicates with a supply taken from the discharge of the fuel pump. When the pump is initially started, the pressure differential bursts the first rupture disc to begin the movement of the piston assembly toward the discharge end. The pressurization of the cartridge causes the second rupture to rupture and hypergolic fluid contained within the cartridge is discharged out the outlet. Once the piston assembly reaches the discharge end of the cartridge, purge grooves allow for fuel and remaining hypergolic fluid, to be discharged out the ignitor outlet into the combustion chamber to purge the ignitor of any remaining hypergolic fluid.

Eric S Taylor↗
NASA NTRS2014

A Battery Health Monitoring Framework for Planetary Rovers

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.

prediction↗
NASA NTRS2005

Hypergolic ignitor

An ignitor for use with the MC-1 rocket engine has a cartridge bounded by two end caps with rupture disc assemblies connected thereto. A piston assembly within the cartridge moves from one end of the cartridge during the ignition process. The inlet of the ignitor communicates with a supply taken from the discharge of the fuel pump. When the pump is initially started, the pressure differential bursts the first rupture disc to begin the movement of the piston assembly toward the discharge end. The pressurization of the cartridge causes the second rupture disc to rupture and hypergolic fluid contained within the cartridge is discharged out the ignitor outlet.

Taylor, Eric S.↗
NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances to spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Moreover, a thermal runaway can propagate from a single "failed" cell to a whole battery pack, resulting in a more serious event. The commercial and automotive sectors need to mitigate thermal runaway events. An electric aircraft (or air taxi) has no alternative in the event of a failure, so preventing such events is paramount. Currently, battery prognostics algorithms only predict the state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle), but do not predict the maximum temperature during a flight or the likelihood of thermal runaway. Our current work focuses on adding the thermal model (based on Birkl's OCV model, in Ref. [1]) to the existing hybrid electrochemical model (single-particle model with lumped parameters as in Ref. [2]) to determine end-of-discharge, end-of-life, and maximum temperature during discharge.

Thermal runaway↗
NASA NTRS1994

Double-ended metal halide arc discharge lamp with electrically isolated containment shroud

A double-ended arc discharge lamp includes a sealed, light-transmissive outer jacket, a light-transmissive shroud mounted within the outer jacket and directly supported by the outer jacket, and an arc discharge tube mounted within the shroud. The arc tube is typically a metal halide arc discharge tube. In a preferred embodiment, the shroud includes an outwardly flared portion at each end. The outwardly flared portions space the shroud from the outer jacket and support the shroud within the outer jacket. The outwardly flared portions of the shroud can be affixed to the outer jacket by fusing. The outer jacket can be provided with inwardly extending dimples for locating the shroud with respect to the outer jacket. In another embodiment, the outer jacket includes reduced diameter portions near each end which are attached to the shroud.

Muzeroll, Martin M.↗
NASA NTRS1994

Double-ended metal halide arc discharge lamp with electrically isolated containment shroud

A double-ended arc discharge lamp includes a sealed, light-transmissive outer jacket, a light-transmissive shroud mounted within the outer jacket and directly supported by the outer jacket, and an arc discharge tube mounted within the shroud. The arc tube is typically a metal halide arc discharge tube. In a preferred embodiment, the shroud includes an outwardly flared portion at each end. The outwardly flared portions space the shroud from the outer jacket and support the shroud within the outer jacket. The outwardly flared portions of the shroud can be affixed to the outer jacket by fusing. The outer jacket can be provided with inwardly extending dimples for locating the shroud with respect to the outer jacket. In another embodiment, the outer jacket includes reduced diameter portions near each end which are attached to the shroud.

Muzeroll, Martin M.↗
NASA NTRS2013

Electrochemistry-based Battery Modeling for Prognostics

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-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. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

battery↗
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 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 NTRS2023

An Accelerated Life Testing Dataset for Lithium-Ion Batteries With Constant and Variable Loading Conditions

The dataset repository is organized into three main folders, each containing one group of life cycled battery packs. Within each folder individual battery packs own their dedicated csv file for continuous data logging, which are named with their respective battery pack number. The folders are named: - regular_alt_batteries: Containing one csv file for each battery pack cycled at the same load level or load range throughout lifetime - recommissioned_batteries: Containing one csv file for each battery pack cycled at different load levels at varying life stages - second_life_batteries: Containing one csv file for each second life battery pack cycled at constant current througout the second life

Li-ion Battery↗
NASA NTRS2010

Performance and Safety Tests of Lithium-Ion Cells Arranged in a Matrix Design Configuration

Matrix Packs display large variations in cell bank voltages at the charge and discharge current (C/2) used in this test program. The voltage difference is larger at the end of discharge than at the end of charge under the conditions studied. Disconnection of a cell from the pack leads to a larger voltage difference during discharge (greater than 2.0 V) between the bank that has one less cell and the other banks. Thermal profile does not show any significant changes or increase in temperature after one cell was disconnected from the bank in spite of falling to very low voltages at the end of discharge. All tests on the matrix pack with the HAM displayed lower max in general due to the placement of thermocouple on the outside of the HAM rather than on the cells. Disconnection of cells has almost no influence on the performance of the packs and does not show any abnormal thermal changes for the 100 cycles obtained in this test program. Longer cycle life may influence the performance especially if the low voltage cell goes into reversal. Overcharge leads to CID activation of cells. If the matrix configuration has a larger number of cells in series, (more than 5 S configuration), the limitations of protective devices may manifest itself irrespective of it being in a matrix configuration. External short circuit causes a fire with expulsion of content from some cells. The fire does not propagate itself laterally, but if there was cell module stacking, then the fire would cause the cells above it to also go into flames/thermal runaway. Limitations of protective devices are observed in this case as the PTCs in the cells did not protect under this abusive condition. Matrix configurations seem to provide protection against lateral propagation of fire and flame. Matrix pack configuration seems to provide good performance in spite of losing cell connections; at least for the configuration tested under this program.

Jeevarajan, Judith↗
NASA NTRS2010

Hazards Due to Overdischarge in Lithium-ion Cylindrical Cells in Multi-cell Configurations

Lithium-ion cells in the cylindrical Commercial-off-the-shelf 18650 design format were used to study the hazards associated with overdischarge. The cells in series or in parallel configurations were subjected to different conditions of overdischarge. The cells in parallel configurations were all overdischarged to 2.0 V for 75 cycles with one cell removed at 25 cycles to study the health of the cell. The cells in series were designed to be in an unbalanced configuration by discharging one cell in each series configuration before the start of test. The discharge consisted of removing a pre-determined capacity from the cell. This ranged from 50 to 150 mAh removal. The cells were discharged down to a predetermined end-of-discharge voltage cutoff which allowed the cell with lower capacity to go into an overdischarge mode. The cell modules that survived the 75 cycles were subjected to one overvoltage test to 4.4 V/cell.

Jeevarajan, Judith↗
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 NTRS2006

Serial Charging Test on High Capacity Li-Ion Cells for the Orbiter Advanced Hydraulic Power System

Although it looks like module level voltage drives the cutoff for charge, the actual cutoff is due to unbalanced cell voltages that drive the module voltage up. Individual cell voltage drives the cutoff for discharge Low resistance cells are the first to reach the low-voltage cutoff Cell-to-Cell voltage differences are generally small and show similar trends for each cycle Increase for a distinct window during charge and at the end of discharge Increase in max to min cell voltage difference with time/cycles Decrease in max to min cell voltage difference during high current pulses with time/cycles Individual cell voltage trends (with respect to other cells) are very repeatable from cycle to cycle, although voltage slowly degrades with time/cycles (resistance growth) Much more difference observed near end of discharge Little change in order of cell voltage (cell with highest voltage to cell with lowest voltage) Temp sensor on the side of cell (between 2 cells) shows much greater rise during discharge than for single cell tests (18 C vs 5 C) Conclusion: Serial Charging of this string of cells is feasible as it has only a minor impact on useful capacity

Jeevarajan, Judith A.↗
NASA NTRS1983

Cycle life test and failure model of nickel-hydrogen cells

Six ampere hour individual pressure vessel nickel hydrogen cells were charge/discharge cycled to failure. Failure as used here is defined to occur when the end of discharge voltage degraded to 0.9 volts. They were cycled under a low earth orbit cycle regime to a deep depth of discharge (80 percent of rated ampere hour capacity). Both cell designs were fabricated by the same manufacturer and represent current state of the art. A failure model was advanced which suggests both cell designs have inadequate volume tolerance characteristics. The limited existing data base at a deep depth of discharge (DOD) was expanded. Two cells of each design were cycled. One COMSAT cell failed at cycle 1712 and the other failed at cycle 1875. For the Air Force/Hughes cells, one cell failed at cycle 2250 and the other failed at cycle 2638. All cells, of both designs, failed due to low end of discharge voltage (0.9 volts). No cell failed due to electrical shorts. After cell failure, three different reconditioning tests (deep discharge, physical reorientation, and open circuit voltage stand) were conducted on all cells of each design. A fourth reconditioning test (electrolyte addition) was conducted on one cell of each design. In addition post cycle cell teardown and failure analysis were performed on the one cell of each design which did not have electrolyte added after failure.

Smithrick, J. J.↗