SEARCH · Search NASA

Results for “Accelerated degradation testing”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well-studied, but the consequences include: offline-modules, -strings, -inverters; system shutdown; arc-faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, attributed to branch connectors. Field-failed specimen assemblies were therefore examined, consisting of cable connector, branch connector and discrete fuse components. Unused field-vintage specimens are presently being examined using combined-accelerated stress testing (C-AST) to clarify the most influential environmental stressors as well as the effect of external mechanical perturbation. A benchtop prototype fixture was used to develop the perturbation capability for C-AST. The benchtop experiments were also used to develop the in-situ data acquisition of specimen: current, voltage, and temperature. A significant increase in operating temperature, ~100 deg C from ~40 deg C, and a different failure mode was observed immediately once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (with failure occurring in the fuses) to 15 A (failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow failure analysis, including the extraction of the internal convolute springs for morphological examination (optical- and electron-microscopy). Chemical composition analysis included: energy-dispersive X-ray spectroscopy, differential scanning calorimetry, and Fourier transform infrared spectroscopy.

balance of systems↗

DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well-studied, but the consequences include offline modules, strings, and inverters; system shutdown; arc faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, originally attributed to branch connectors. Field-failed specimen assemblies were therefore examined, consisting of cable connector, branch connector, and discrete fuse components. In this study, unused field-vintage specimens are examined using combined-accelerated stress testing (C-AST) to clarify the most influential environmental stressors as well as the effect of external mechanical perturbation. A benchtop prototype fixture was used to develop the perturbation capability for the C-AST chamber. The benchtop experiments were also used to develop the in-situ data acquisition of specimen current, voltage, and temperature. A significant increase in operating temperature (~100 degrees C from ~40 degrees C) and a different failure mode were observed promptly once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (with failure in the fuses) to 15 A (failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow for failure analysis, including the extraction of the internal convolute springs for morphological examination (optical and electron microscopy).

balance of systems↗

DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗

DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing

balance of systems↗

DOE OSTI2021

R&D to Ensure a Scientific Basis for Qualification Tests and Standards (Final Report: FY19-FY22)

This is the final technical report for the FY19-FY21 research program "R&D to Ensure a Scientific Basis for Qualification Tests and Standards." This research was performed because project return on investment in a photovoltaic (PV) system depends increasingly on maintaining high energy yields, and the system lifetime is a major factor in levelized cost of electricity (LCOE). Thus, the rate of PV deployment and the success of these assets depends upon reliable long-term power generation. This program advances PV module reliability of commercial products through identification of reliability needs, characterization that provides scientific understanding of targeted degradation mechanisms, and translation of those data into practical and predictive test protocols and standards. This report describes program scope, results, milestones, and accomplishments over the three-year period of performance.

14 SOLAR ENERGY↗

DOE OSTI2016

PV degradation curves: non‐linearities and failure modes

Abstract Photovoltaic (PV) reliability and durability have seen increased interest in recent years. Historically, and as a preliminarily reasonable approximation, linear degradation rates have been used to quantify long‐term module and system performance. The underlying assumption of linearity can be violated at the beginning of the life, as has been well documented, especially for thin‐film technology. Additionally, non‐linearities in the wear‐out phase can have significant economic impact and appear to be linked to different failure modes. In addition, associating specific degradation and failure modes with specific time series behavior will aid in duplicating these degradation modes in accelerated tests and, eventually, in service life prediction. In this paper, we discuss different degradation modes and how some of these may cause approximately linear degradation within the measurement uncertainty (e.g., modules that were mainly affected by encapsulant discoloration) while other degradation modes lead to distinctly non‐linear degradation (e.g., hot spots caused by cracked cells or solder bond failures and corrosion). The various behaviors are summarized with the goal of aiding in predictions of what may be seen in other systems. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Jordan, Dirk C.↗

DOE OSTI2022

Electrochemical characterization of evolving ionomer/electrocatalyst interactions throughout accelerated stress tests

Here, the degradation of polymer electrolyte membrane fuel cells (PEMFCs) catalyst layers used for heavy-duty vehicles was examined using a catalyst-specific accelerated stress test (AST). High surface area carbon supported dispersed Pt (Pt/HSC), annealed Pt (a-Pt/HSC) and PtCo (PtCo/HSC) alloy catalysts were examined over the course of 90,000 cycles by measuring changes in mass activity, O 2 transport resistance, electrochemical active surface area (ECSA), catalyst accessibility and ionomer-electrocatalyst interactions. Compared to a-Pt/HSC and Pt/HSC catalyst, the PtCo/HSC showed better initial mass activity, a larger initial mass transport loss, and faster degradation after the first 30k AST cycles, as a large portion of Co leached out during potential cycling. Pt/HSC showed higher initial performance relative to a-Pt/HSC but had faster degradation. STEM characterizations show that the ECSA losses are largely related to Pt dissolution resulting in either catalyst particle growth via the Ostwald ripening mechanism or redeposition in the membrane. Catalyst accessibility measurements showed decreased RH sensitivity for all three samples, while CO impedance measurements revealed a decrease in both Pt-water and carbon-water interactions. This implies that, Pt is either preferentially redepositing on the exterior of the carbon support, or that the ionomer is undergoing morphological changes enabling the enhanced intrusion of mesopores.

25 ENERGY STORAGE↗

DOE OSTI2024

Investigating the Crosslinking, Degradation, and Adhesion Behavior of Photovoltaic Encapsulants Under Thermal Accelerated Aging

Degradation of photovoltaic (PV) module encapsulant characteristics that lead to mechanical embrittlement and delamination remains a cause of failure in solar installations. A multiscale reliability model connecting the encapsulant mechanical and fracture properties to the degraded molecular structure and interfacial bonding to adjacent solar cell and glass substrates was previously published. The model, developed primarily for poly(ethylene-co-vinyl acetate) acetate (EVA) encapsulants, remains to be experimentally validated. Determining the degradation and crosslinking kinetics of alternative encapsulants, such as polyolefin elastomer (POE) and EVA/POE/EVA composites (EPE), can generalize the model. In this work, we subject fully cured EVA, POE, and EPE encapsulants to accelerated thermal aging to determine how high temperatures impact reaction kinetics. An increase in gel content (crosslinking) and decrease in crystallinity of the encapsulants under hot-aerobic (90 degrees C, 22% RH) and hot-anaerobic (90 degrees C, sealed in N 2 air) aging were observed, even in the absence of UV and crosslinking initiators. Fourier transform infrared spectroscopy (FTIR)-attenuated total reflectance analysis showed insignificant encapsulant degradation, demonstrating the critical role of UV and moisture in accelerating degradation. Adhesion testing performed on coupon-level specimens (cell/encapsulant/glass laminates) showed decreases in adhesion energy, Gc , from 5000 h of hot-dry (90 degrees C, ~1% RH) and hot-humid (90 degrees C, 60% RH) aging. POE coupons demonstrated the best stability, followed by EPE then EVA. For EVA and POE, hot-humid aged coupons experienced a larger decrease in Gc due to enhanced hydrolytic degradation. Hot-dry aging condition demonstrated that thermal degradation of the interface could be significant even if the encapsulant experiences negligible degradation in the absence of UV and elevated humidity.

accelerated aging↗

DOE OSTI2024

Highly accelerated life testing (HALT): A review from a statistical perspective

Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated-stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). Here, this review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high-stress test-analyze-fix-test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Enabling Ambient Stability of LiNiO 2 Lithium-Ion Battery Cathode Materials via Graphene–Cellulose Composite Coatings

Nickel-rich layered oxides are widely used as cathode materials for energy-dense lithium-ion batteries. These chemistries, based on the parent compound LiNiO 2 (LNO), are highly sensitive to ambient environments and are known to readily react with moisture and carbon dioxide. As a result, impurities such as lithium hydroxides and lithium carbonates are formed at the LNO surface, compromising electrochemical behavior. Here, we address this issue by coating LNO cathode particles with a hydrophobic barrier layer composed of graphene and ethyl cellulose (GrEC). This coating limits contact between atmospheric moisture and the LNO surface, which minimizes the generation of lithium impurities. This scheme is evaluated by exposing coated LNO to humidified CO 2 for 24 h as an accelerated ambient degradation test. Subsequent spectroscopy, microscopy, and electrochemical characterization show no detectable signatures of carbonates on the LNO surface, thus verifying that the GrEC coating prevents ambient degradation. In conclusion, by demonstrating this methodology for the ultimate nickel-rich chemistry, this approach can likely be generalized to a wide range of ambient-sensitive battery materials.

25 ENERGY STORAGE↗

DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗

DOE OSTI2023

Accelerated Stress Testing of Perovskite Photovoltaic Modules: Differentiating Degradation Modes with Electroluminescence Imaging

Herein, electroluminescence (EL) and thermal imaging are used to examine p–i–n metal halide perovskite (MHP) photovoltaic (PV) mini-modules (MA0.6FA0.4PbI3, 20 cells, 78 cm2) before and after indoor-accelerated stress testing or outdoor deployment. Distinct spatial patterns in the EL images emerge, which depend on the external stress conditions experienced by the mini-module. Imaging results highlight a distribution of dark speckle features that dominate after UV stress, attributed to widespread interfacial contact degradation. Lateral intensity gradients across cells dominate after thermal cycling (TC) stress, attributed to current crowding near scribe defects. While current–voltage analysis alone does not give full insight on the degradation process, this study shows that distinct degradation modes can be further defined by multimodal electro-optical imaging (i.e., EL combined with photoluminescence and dark lock-in thermography). Neither UV exposure nor TC-accelerated stress testing alone replicates the same degradation signatures observed after outdoor deployment, suggesting that multiple degradation modes occur under concurrent stressors outdoors. Finally, spatial characterization of degradation modes in MHP PV mini-modules before and after accelerated stress testing lays the groundwork for developing targeted accelerated stress testing procedures through comparison with outdoor aging.

14 SOLAR ENERGY↗

DOE OSTI2022

Evaluating the Durability of Balance of Systems Components Using Combined-Accelerated Stress Testing: Preprint

The degradation of photovoltaic (PV) balance of systems (BoS) components is not well-studied, but the consequences include: offline-modules, -strings, -inverters; system shutdown; arc-faults; and fires. A utility provider experienced a ~30% failure rate in their power transfer chain, originally attributed to branch connectors. Field-failed specimen assemblies were therefore examined, consisting of cable connector, branch connector, and discrete fuse components. Unused field-vintage specimens are presently being examined using combined-accelerated stress testing (C-AST) to clarify the most influential environmental stressors as well as the effect of external mechanical perturbation. A benchtop prototype fixture was used to develop the perturbation capability for the C-AST chamber. The benchtop experiments were also used to develop the in-situ data acquisition of specimen: current, voltage, and temperature. A significant increase in operating temperature, ~100 deg C from ~40 deg C, and a different failure mode was observed promptly once periodic mechanical perturbation was applied. The current at failure was decreased from 35 A (with failure in the fuses) to 15 A (failure at the male/female metal pin connection). After initial examination using X-ray computed tomography, the external plastic was machined away from failed specimens to allow failure analysis, including the extraction of the internal convolute springs for morphological examination (optical- and electron-microscopy).

balance of systems↗

DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗

NASA NTRS1981

Accelerated testing for synchronous orbits

Degradation of batteries during synchronous orbits is analyzed. Discharge and recharge rates are evaluated. The functional relationship between charge rate and degradation is mathematically determined.

Mcdermott, P.↗

NASA NTRS1986

Long-term stability of amorphous-silicon modules

The Jet Propulsion Laboratory (JPL) program of developing qualification tests necessary for amorphous silicon modules, including appropriate accelerated environmental tests reveal degradation due to illumination. Data were given which showed the results of temperature-controlled field tests and accelerated tests in an environmental chamber.

Ross, R. G., Jr.↗

DOE OSTI2024

Field‐representative evaluation of PID‐polarization in TOPCon PV modules by accelerated stress testing

Abstract Potential‐induced degradation‐polarization (PID‐p) can reduce module power, but how to project the extent to which PID‐p may occur in field conditions considering the factors of system voltage, condensed moisture, temperature, and illumination has not been clarified. Using tunnel oxide passivated contact (TOPCon) modules, this work demonstrates a method to test full‐size crystalline silicon PV modules for PID‐p to provide field‐representative results. In initial screening tests with positive or negative 1000 V electrical bias applied at 60°C for 96 h using Al foil electrodes on the glass surfaces, the module type exhibited reversible PID‐p only on the front face when the cell circuit was in negative voltage potential. No PID was detected on the rear after testing in either polarity. We then evaluated the PID‐p sensitivity on the front side under different UV irradiances while maintaining the glass surface wet to estimate real‐world susceptibility to PID‐p. The magnitude of the observed behavior was fit using a previously developed charge transfer and depletion by light model. Whereas power loss with −1000 V applied to the cell circuit at 60°C for 96 h in the dark was about 30%, testing the module front under 0.051 W·m −2 nm −1 at 340 nm UVA irradiation using fluorescent tubes, the mean degradation was only 3%. When the modules were tested in the dark for PID‐p with in situ dark current–voltage (I‐V) characterization, the thermal activation energy for degradation was 0.71 eV; for recovery in the dark, it was 0.58 eV. Whereas recovery from the degraded state at 60°C in the dark without voltage bias was 5% absolute in 38 h, rapid recovery of about 5% absolute was observed with 1000 W·s/m 2 exposure at 25°C using a flash tester.

14 SOLAR ENERGY↗

DOE OSTI2020

Auto-BLAST (AutoBLAST) [SWR-20-93]

Battery life modeling often involves a lot of manual parameter fitting and is not easy for users to adopt the model and use it. To reduce the difficulties for users to adopt battery lifetime models, an automatic battery lifetime modeling analysis and simulation tool suite, Auto-BLAST, has been developed. Auto-BLAST includes a lithium-loss-base life model, a battery electric model, and an algorithm which automatically fits all key parameters in the electric and life models using user provided data. The models and algorithm are packaged into two user-friendly GUIs, Auto-LifeMod, for easy battery life prognostic model fitting and Auto-LifeSim, for easy battery lifetime simulation. The lithium-loss-based life model adopts a similar model framework that models degradations using aging rate models, using battery cycling data to predict battery degradation and expected lifetime. The electric model is an equivalent circuit model which simulates battery voltage responses based on current/power demand profiles. The auto-fitting algorithm uses the user-input data to generate custom battery life model(s). Two GUIs, wrapping around the life model and the auto-fitting algorithm, provide friendly interfaces for users to generate a life model and use it for case study. One GUI requires summary data from accelerated battery life degradation tests as an input and produces battery life models predicting (a) capacity degradation, and (b) resistance growth of the battery. The GUI displays the electrical model response and the input experimental data against the life model predictions with fitted model parameters and fitting error. The GUI also generates an output file to save the fitted model parameters. The second GUI uses the life model from the first GUI and a user-defined battery cycling profile to predict battery lifetime degradation and expected lifetime. Predicted capacity and resistance vs. time are plotted in the GUI and saved to output file. There is a user guide for both GUIs

Mishra, Partha↗