Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Calendar aging”

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.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Role of Coatings as Artificial Solid Electrolyte Interphases on Lithium Metal Self-Discharge

Artificial solid electrolyte interphases have provided a path to improved cycle life for high energy density, next-generation anodes like lithium metal. Although long cycle life is necessary for widespread implementation, understanding and mitigating the effects of aging and self-discharge are also required. In this report we investigate several coating materials and their role in calendar life aging of lithium. We find that the oxide coatings are electronically passivating whereas the LiF coating slows charge transfer kinetics. Furthermore, the Coulombic loss during self-discharge measurements improves with the oxide layers and worsens with the LiF layer. It is found that none of the coatings create a continuous conformal, electronically passivating layer on top of the deposited lithium nor are they likely to distribute evenly through a porous deposit, suggesting that none of the materials are acting as an artificial solid electrolyte interphase. Instead, they likely alter performance through modulating lithium nucleation and growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On the Efficacy of Repeat Voltage Holds for Conditioning and Calendar Life Testing of Graphite and Silicon Cells

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

25 ENERGY STORAGE↗
DOE OSTI2026

Mechanistic Understanding of Interphase-driven Aging in Silicon Anodes

Conventional solid electrolyte interphases (SEIs) strongly adhere to micro-silicon (µ-Si) and crack under volume changes, causing poor cycling performance. Nano-silicon improves cycling performance but remains costly with limited calendar life. Here potentiostatic ageing tests demonstrate that both calendar and cycle ageing are governed by SEI cracking and dissolution with different relative contributions. When the system is not dominated by SEI dissolution, the relative calendar life of Si anodes could correlates positively with their cycle life. LiF-rich SEI that enables long cycle life in µ-Si is therefore expected to enhance calendar life as well. Using this framework, we screened electrolytes, SEIs and electrodes and validated them with full-cell storage. LiF-rich SEI minimizes cracking and dissolution, enabling μ-Si to achieve excellent calendar life, whereas nano-silicon suffers from SEI dissolution and needs reduced electrolyte–electrode contact for better calendar life. This work clarifies calendar-ageing behaviour and accelerates electrolytes and SEI development for long-life Si anodes.

Johnson, Christopher S.↗
DOE OSTI2013

Low Activation Joining of SiC/SiC Composites for Fusion Applications: Tape Casting TiC+Si Powders

The use of SiC composites in fusion environments likely requires joining of plates using reactive joining or brazing. One promising reactive joining method uses solid-state displacement reactions between Si and TiC to produce Ti3SiC2 + SiC. We continue to explore the processing envelope for this joint for the TITAN collaboration in order to produce optimal joints to undergo irradiation studies in HFIR. One noted feature of the joints produced using tape-calendared powders of TiC+Si has been the large void regions that have been apparently unavoidable. Although the produced joints are very strong, these voids are undesirable. In addition, the tapes that were made for this joining were produced about 20 years ago and were aging. Therefore, we embarked on an effort to produce some new tape cast powders of TiC and Si that could replace our aging tape calendared materials.

Fusion↗
DOE OSTI2021

Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning

A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real-world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2% mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5% of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real-world conditions due to the impact of calendar degradation.

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium-Ion Battery Life Model with Electrode Cracking and Early-Life Break-in Processes

This paper develops a physically justified reduced-order capacity fade model from accelerated calendar- and cycle-aging data for 32 lithium-ion (Li-ion) graphite/nickel-manganese-cobalt (NMC) cells. The large data set reveals temperature-, charge C-rate-, depth-of-discharge-, and state of charge (SOC)-dependent degradation patterns that would be unobserved in a smaller test matrix. Model structure is informed by incremental capacity analysis that shows loss of lithium inventory and cathode-material loss as the dominant capacity fade mechanisms. The model includes terms attributable to solid-electrolyte interface (SEI) growth, electrode cracking, cycling-driven acceleration of SEI growth, and "break-in" mechanisms that slightly decrease or increase available Li inventory early in life. The study explores what mathematical couplings of these mechanisms best describe calendar aging, cycle aging, and mixed calendar/cycle aging. Various approaches are discussed for extracting relevant stress factors from complex cycling profiles to predict lifetime during real-world battery loads using models trained on constant-current laboratory test results. The complexity of the present human-driven model identification process motivates future work in machine learning to more widely search and statistically discern the optimal model that correctly extrapolates capacity fade based on physical knowledge.

25 ENERGY STORAGE↗
DOE OSTI2025

The Origin of Improved Performance in Boron‐Alloyed Silicon Nanoparticle‐Based Anodes for Lithium‐Ion Batteries

Stabilizing the solid electrolyte interphase (SEI) remains a key challenge for silicon‐based lithium‐ion battery anodes. Alloying silicon with secondary elements like boron has emerged as a promising strategy to improve the cycle life of silicon anodes, yet the underlying mechanism remains unclear. To address this knowledge gap, how boron concentration influences battery performance is systematically investigated. These results show a near‐monotonic increase in cycle lifetime with higher boron content, with boron‐rich electrodes significantly outperforming pure silicon. Additionally, silicon‐boron alloy anodes exhibit nearly three times longer calendar life than pure silicon. Through detailed mechanistic analysis, alternative contributing factors are systematically ruled out, and it is proposed that improved passivation arises from a strong permanent dipole at the nanoparticle surface. This dipole, formed by undercoordinated and highly Lewis acidic boron, creates a static, ion‐dense layer that stabilizes the electrochemical interface, reducing parasitic electrolyte decomposition and enhancing long‐term stability. These findings suggest that, within the SEI framework, the electric double layer is an important consideration in surface passivation. This insight provides an underexplored parameter space for optimizing silicon anodes in next‐generation lithium‐ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2019

Modeling memoryless degradation under variable stress

Accelerated degradation tests can be used as the basis for predicting the performance or state of health of products and materials at use conditions over time. Measurements acquired at accelerated levels of stress are used to develop models that relate to the degradation of one or more performance measures. Frequently, products/materials of interest are subjected to variable stress levels during their lifetimes. However, testing is usually performed only at a few fixed stress levels. In such cases, cumulative degradation models are developed and assessed by using data acquired under those fixed stress conditions. The degradation rate at any stress condition within the range of the model can be estimated by the derivative of the cumulative model at that stress condition. It follows that, to predict cumulative degradation over variable use conditions, one might integrate the fluctuating degradation rate over time. Existing approaches for doing this consider degradation rates that depend only on the current stress level. Here, we propose to allow the degradation rate to also depend on the current state of health as indicated by the associated performance measure(s). The resulting modeling approach is capable of portraying a broader range of degradation behavior than existing approaches. The assertion of memoryless degradation by using this or any other approach should be assessed experimentally with data acquired under variable stress in order to increase confidence that the integrated rate model is accurate. In this article, we demonstrate the additional capability of the proposed approach by developing empirical memoryless rate-based degradation models to predict resistance increase and capacity decrease in lithium-ion cells that are being evaluated for use in electric vehicles. Here, we then assess the plausibility of these models.

42 ENGINEERING↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Non-thyroid cancer incidence in Belarusian residents exposed to Chernobyl fallout in childhood and adolescence: Standardized Incidence Ratio analysis, 1997–2011

Background: While an increased risk of thyroid cancer from post-Chernobyl exposure to Iodine-131 (I-131) in children and adolescents has been well-documented, risks of other cancers or leukemia as a result of residence in radioactively contaminated areas remain uncertain. Methods: We studied non-thyroid cancer incidence in a cohort of about 12,000 individuals from Belarus exposed under age of 18 years to Chernobyl fallout (median age at the time of Chernobyl accident of 7.9 years). During 15 years of follow-up from1997 through 2011, 54 incident cancers excluding thyroid were identified in the study cohort with 142,968 person-years at risk. We performed Standardized Incidence Ratio (SIR) analysis of all solid cancers excluding thyroid (n=42), of leukemia (n=6) and of lymphoma (n=6). Results: We found no significant increase in the incidence of non-thyroid solid cancer (SIR=0.83, 95% Confidence Interval [CI]: 0.61; 1.11), lymphoma (SIR=0.66, 95% CI: 0.26; 1.33) or leukemia (SIR=1.78, 95% CI: 0.71; 3.61) in the study cohort as compared with the sex-, age- and calendar-time-specific national rates. These findings may in part reflect the relatively young age of study subjects (median attained age of 33.4 years), and long latency for some radiation-related solid cancers. Conclusions: We found no evidence of statistically significant increases in solid cancer, lymphoma and leukemia incidence 25 years after childhood exposure in the study cohort; however, it is important to continue follow-up non-thyroid cancers in individuals exposed to low-level radiation at radiosensitive ages. - Highlights: • We monitor cancers in a Belarusian cohort of exposed as children due to Chernobyl. • No increase in solid cancer rates was found as compared to the national rates. • An elevation of leukemia rates was detected, although statistically insignificant. • Results are consistent with those in a cohort of exposed as children in Ukraine. • Further monitoring of cancer situation in this cohort is warranted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Significant Improvements to Si Calendar Lifetime Using Rapid Electrolyte Screening via Potentiostatic Holds

Silicon-based lithium-ion batteries exhibit severe time-based degradation resulting in poor calendar lives. This has been identified as the major impediment towards commercialization with cycle life considered a solved issue through nanosizing and protective coatings allowing over 1000 cycles of life to be achieved. In this work, rapid screening of sixteen electrolytes for calendar life extension of Si-rich systems (70 wt% Si) is performed using the voltage hold (V-hold) protocol. V-hold significantly shortens the testing duration over the traditional open circuit voltage reference performance test allowing us to screen electrolytes within a span of two months. We find a novel ethylene carbonate (EC) free electrolyte formulation containing lithium hexafluorophosphate (LiPF 6 ) salt, and binary solvent mix of fluoroethylene carbonate (FEC), ethyl methyl carbonate (EMC) that extends calendar life of Si cells as compared to conventional EC based electrolyte. Our coupled experimental-theoretical analysis framework provides a decoupling of the parasitic currents during V-hold, allowing us to extrapolate the capacity loss to predict semiquantitative calendar lifetimes. Subsequently, cycle aging and oxidative stability tests of the EC free system also show enhanced performance over baseline electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗
DOE OSTI2018

Overall and cause‐specific mortality in the Sentinel system: A power analysis

Abstract Purpose Mortality data within the Sentinel Death Tables remain generally uncharacterized. Assessment of mortality data within Sentinel will help inform its utility for medical product safety studies. Methods To determine if Sentinel contains sufficient all‐cause and cause‐specific mortality events to power postmarketing safety studies. We calculated crude rates of all‐cause mortality and suicide and proportional mortality from suicide from 2004 to 2012 in seven Sentinel data partners. Results were stratified by data partner, sex, age group, and calendar year and compared with national estimates from Centers for Disease Control and Prevention Wide‐ranging ONline Data for Epidemiologic Research. We performed sample size estimations for all‐cause mortality and 10 leading causes of death. Results We observed 479 694 deaths, including 5811 suicides, during 68 million person‐years of follow‐up. Pooled mean death and suicide rates in the data partners were 710 and 8.6 per 100 000 person‐years, respectively (vs 810 and 11.8 nationally). The mean proportional mortality from suicide among the data partners was 1.2%, compared with 1.5% nationally. National trends of decreasing overall mortality and increasing proportional mortality for suicide were reflected within Sentinel. We estimated that detecting hazard ratios of 1.25 and 3 would require 16 442 and 460 exposed patients, respectively, for overall mortality, and 1.3 million and 37 411, respectively, for suicide. Conclusions This was the first study to investigate mortality data in the Sentinel death tables. We found that all‐cause mortality appeared well powered for use as a safety outcome and cause‐specific mortality outcomes may be adequately powered in certain circumstances. Further investigation into the quality of the Sentinel death data is needed.

Swain, Richard S.↗
DOE OSTI2020

Shell Predictive Battery Life Models (SPBLM) [SWR-20-35]

Battery life models to predict the capacity fade and resistance growth of lithium-ion batteries from various manufacturers with different chemistries. The battery life model is trained on experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. The battery life model is then used to predict capacity fade and resistance growth under any arbitrary aging scenario.

Smith, Kandler↗
DOE OSTI2021

PyBLM (Python-based Battery Life Model) [SWR-21-50]

PyBLM is a battery life model to predict the capacity fade of two home battery energy storage systems, manufactured by LG and Tesla. The battery life model is built from experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. PyBLM is developed in Python and formatted to work in conjunction with another NREL software named TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies).

Mishra, Partha↗
NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

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 spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗
DOE OSTI2015

Predictive Models of Li-ion Battery Lifetime

It remains an open question how best to predict real-world battery lifetime based on accelerated calendar and cycle aging data from the laboratory. Multiple degradation mechanisms due to (electro)chemical, thermal, and mechanical coupled phenomena influence Li-ion battery lifetime, each with different dependence on time, cycling and thermal environment. The standardization of life predictive models would benefit the industry by reducing test time and streamlining development of system controls.

25 ENERGY STORAGE↗
DOE OSTI2017

Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries

For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately.For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1 % of the original cell capacity.

25 ENERGY STORAGE↗