NiCd battery cycle life prediction equation for low Earth orbit
Cycle life analysis of the nickel cadmium battery is given. The life prediction equation is limited to spacecraft in low Earth orbits.
Hafen, D.↗
SEARCH · Search NASA
Results for “cycle life prediction”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Systematic feature design for cycle life prediction of lithium-ion batteries during formation
Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.
25 ENERGY STORAGE↗
Mission Cycle Life Prediction for C/SiC Composites
The residual strength and life of C/SiC is dominated by carbon interface and fiber oxidation if thermal and mechanical stresses combine with thermal expansion mismatch in a manner such that seal coat and matrix cracks are open to allow oxygen ingress. Under these circumstances life can be predicted by simple oxidation based models based on reaction controlled kinetics at low temperature and gas phase diffusion controlled kinetics at high temperature. Key life governing variables in these models include temperature, applied stress, initial strength, oxygen partial pressure and total pressure. In this paper these models are described and extended to mission cycle life prediction.
Levine, Stanley R.↗
Battery Cycle Life Prediction Across a Variety of Datasets
Explore the source record for details and available documents.
Lubars, Joseph↗
Leveraging Battery Archive to Evaluate Cycle Life Prediction Methods Across a Variety of Datasets
Explore the source record for details and available documents.
Lubars, Joseph↗
Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.
25 ENERGY STORAGE↗
Oxidation and low cycle fatigue life prediction
When a metallic material is exposed to a high temperature in an ambient atmosphere, oxidation takes place on the metallic surface. The formed oxides (both surface and grain boundary oxides) are mechanically brittle so that if the stress is high enough the oxides will be cracked. The grain boundary oxide formation in TAZ-8A nickel-base superalloy was studied. The effect of oxide crack nucleus on low cycle fatigue life will be analyzed. The TAZ-8A was subjected to high temperature oxidation tests in air under the stress-free condition. The oxidation temperatures were 600, 800, and 1000 C. The oxidation time varies from 10 to 1000 hours.
Oshida, Y.↗
Designing a Monte Carlo Model with Python to Predict the Life Cycle Disposal Costs for Grid-Level Electrical Energy Storage Systems.
Abstract not provided.
Roberts-Baca, Samuel↗
Battery calendar aging and machine learning
Batteries continue to attract immense attention due to the critical need for new energy technologies that can help humankind transition to a net-zero future. To meet global goals for implementation of new batteries, the research community needs to more coherently consider how to go about the process of innovation, development, and validation of new technologies. Current practices for bringing a new battery technology from the benchtop to deployment can be very time consuming and complex, given that batteries can have distinct aging pathways based on both how they are cycled and their storage conditions. In nearly all use cases, batteries experience periods of active cycling and also extended periods of rest. Thus, calendar time often occurs when a battery is close to fully charged. Currently, the combined cycle and calendar life aspects receive inconsistent attention during most stages of research and development. For batteries to fulfill the critical role envisioned to meet global energy demands, greater uniformity in practice is needed to alleviate potential delays caused by the inconsistent acquisition of aging data. In this study, we present our thoughts and recommendations on the need to further advance commonality and availability of calendar aging data for emerging high-energy and long-duration batteries. While there is a broad need to adopt best practices for both calendar and cycle life assessment, a push to greater uniformity in approach and reporting is sorely needed to allow calendar life predictions to harness many of the advances that have recently been seen for cycle life prediction using machine learning (ML) and other techniques.
25 ENERGY STORAGE↗
Monte Carlo simulation by computer for life-cycle costing
Prediction of behavior and support requirements during the entire life cycle of a system enables accurate cost estimates by using the Monte Carlo simulation by computer. The system reduces the ultimate cost to the procuring agency because it takes into consideration the costs of initial procurement, operation, and maintenance.
Gralow, F. H.↗
Physics-Based Analysis of Cell Imbalances and Aging in Lithium-Ion Battery Modules and Packs
Lithium-ion battery (LIB) packs are a key solution for grid-scale energy storage, enabling grid resilience and supporting critical infrastructure. LIB modules and packs experience current imbalances and uneven cell aging due to various design and operational factors, and require a battery management system (BMS) to continuously monitor and control. In this context, a physics-based modeling framework for LIB modules and packs (liionpack) was enhanced to identify design and control strategies that minimize current imbalance and improve module/pack operation. Simulations of an 8-cell parallel-connected module demonstrate that reducing current imbalance leads to more uniform cell aging and improved module/pack-level degradation predictions. The analysis shows that current imbalance are affected by the electrical resistances. Terminal location significantly affects imbalance, with opposite-end terminal connections at intermediate branches minimizing the imbalance, and the pack circuit construction influences the accuracy of physics-based analysis at the pack scale. This framework enables design optimization of modules and packs through a fast and easy evaluation of pack performance and aging, and supports the development of aging-informed balancing strategies compatible with BMS implementation. Thereby, offering practical pathways to improve reliability and cycle life predictions in large-scale battery energy storage systems.
Ayalasomayajula, Surya Mitra [Oak Ridge National L↗
Evaluation program for secondary spacecraft cells
A life cycle test of secondary electric batteries for spacecraft applications was conducted. A sample number of nickel cadmium batteries were subjected to general performance tests to determine the limit of their actual capabilities. Weaknesses discovered in cell design are reported and aid in research and development efforts toward improving the reliability of spacecraft batteries. A statistical analysis of the life cycle prediction and cause of failure versus test conditions is provided.
Christy, D. E.↗
Accelerated test modeling
Cycle life regression model, cycle life prediction model, and acceleration factors are discussed. A method was presented to: (1) select a mathematical model; (2) determine model coefficients using accelerated test data; (3) test model fit of the accelerated test data; and (4) predict normal packs.
Schwartz, D.↗
Evaluation program for secondary spacecraft cells: Cycle life test
The service life and storage stability for several storage batteries were determined. The batteries included silver-zinc batteries, nickel-cadmium batteries, and silver-cadmium batteries. The cell performance characteristics and limitations are to be used by spacecraft power systems planners and designers. A statistical analysis of the life cycle prediction and cause of failure versus test conditions is presented.
Harkness, J. D.↗
SSME main combustion chamber life prediction
Typically, low cycle fatigue life is a function of the cyclic strain range, the material properties, and the operating temperature. The reusable life is normally defined by the number of strain cycles that can be accrued before severe material degradation occurs. Reusable life is normally signified by the initiation or propagation of surface cracks. Hot-fire testing of channel wall combustors has shown significant mid-channel wall thinning or deformation during accrued cyclic testing. This phenomenon is termed cyclic-creep and appears to be significantly accelerated at elevated surface temperatures. This failure mode was analytically modelled. The cyclic life of the baseline SSME-MCC based on measured calorimeter heat transfer data, and the life sensitivity of local hot spots caused by injector effects were determined. Four life enhanced designs were assessed.
Cook, R. T.↗
Application of cyclic damage accumulation life prediction model to high temperature components
A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, but it can be applied to other materials as well. The method is designed to account for the effects on creep-fatigue life of complex loading such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features of this model were developed to make it practical for application to actual component analysis, such as the ability to handle nonisothermal loading (including TMF), arbitrary cycle paths, and multiple damage modes. The CDA life prediction model was derived from extensive specimen tests conducted on cast nickel-base superalloy B1900 + Hf. These included both monotonic tests (tensile and creep) and strain-controlled fatigue experiments (uniaxial, biaxial, TMF, mixed creep-fatigue, and controlled mean stress). Additional specimen tests were conducted on wrought INCO 718 to verify the applicability of the final CDA model to other high-temperature alloys. The model will be available to potential users in the near future in the form of a FORTRAN-77 computer program.
Nelson, Richard S.↗
Life prediction modeling based on cyclic damage accumulation
A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, where initiation was defined as a 0.030 inch surface length crack. A principal engineering feature of the CDA method is the minimum data base required for implementation. Model constants can be evaluated through a few simple specimen tests such as monotonic loading and rapic cycle fatigue. The method was expanded to account for the effects on creep-fatigue life of complex loadings such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. A significant data base was generated on the behavior of the cast nickel-base superalloy B1900+Hf, including hundreds of specimen tests under such loading conditions. This information is being used to refine and extend the CDA life prediction model, which is now nearing completion. The model is also being verified using additional specimen tests on wrought INCO 718, and the final version of the model is expected to be adaptable to most any high-temperature alloy. The model is currently available in the form of equations and related constants. A proposed contract addition will make the model available in the near future in the form of a computer code to potential users.
Nelson, Richard S.↗
The Effect of Drive Signal Limiting on High Cycle Fatigue Life Analysis
It is common practice to assume a Gaussian distribution of both the input acceleration and the response when modeling random vibration tests. In the laboratory, however, shaker controllers often limit the drive signal to prevent high amplitude peaks. The high amplitudes may either be truncated at a given level (socalled brick wall limiting or abrupt clipping), or compressed (soft limiting), resulting in drive signals which are no longer Gaussian. The paper first introduces several methods for limiting a drive signal, including brick wall limiting and compression. The limited signal is then passed through a linear time-invariant system representing a device under test. High cycle fatigue life predictions are subsequently made using spectral fatigue and rainflow cycle counting schemes. The life predictions are compared with those obtained from unclipped input signals. Some guidelines are provided to help the test engineer decide how clipping should be applied under different test scenarios.
Kihm, Frederic↗