Calendar Age Research Articles

Epidemiology and clinical correlates of hidradenitis suppurativa in primary care in Italy.

Hidradenitis suppurativa (HS) is a persistent skin disorder that is characterized by painful lesions or pus-filled lumps, mostly occurring in areas where the skin flexes. It is a disfiguring condition that significantly reduces the quality of life of those affected. Developing new, effective treatments for HS is crucial, but it is important that it be recognized and diagnosed early, especially in primary care settings. To assess the epidemiology and clinical correlates of HS in a primary care setting. The study utilized the Italian Health Search Database (HSD). A case-control design was adopted to investigate the clinical correlates of HS. Cases were classified as either "definite" or "probable" using an operational algorithm. Up to 10 controls were matched to each case based on factors such as calendar period, age, sex, and duration of follow-up. Cumulative prevalence of HS increased from 0.06% in 2002 to 0.46% in 2021. When only "definite" cases were considered, the prevalence was almost 10 times lower (0%-0.02%). Several clinical correlates were found to be positively associated with HS, including obesity, dyslipidemia, hypertension, autoimmune/inflammatory diseases, and depression. This study found that correct diagnoses of HS were made, as demonstrated by the expected relationship with clinical correlates. These associations were consistent when probable cases were included in the analysis. This evidence could serve as a foundation for proposing a decision support system for general practitioners to help identify HS in individuals with certain coexisting conditions.

A Markovian Aging Process Forecasting Model: Predicting U.S. Mortality

In this study, we provide a novel finite-state Markov model for predicting death rates. The Markovian physiological age, which forms the basis of this model, is represented by the states in the underlying continuous-time Markov chain. This model forecasts mortality rates for the Markovian physiological age, for which mortality rates for calendar ages can be easily computed. A set of data from the U.S. population is used to calibrate the model. The data collection includes individuals aged 30 to 108 and covers the years 1970 to 2019. We train the model using data from 1970 to 2014 and then test it using data from 2015 to 2019. Based on metrics utilized for training and test datasets, the suggested model outperforms the models of both Lee and Carter (1992) and Renshaw and Haberman (2006). It is noteworthy that this method uses fewer model parameters than the comparable models. Forecasts of mortality rates and life expectancy are made using the findings. According to the findings, a 30-year-old’s life expectancy in 2040, 2060, and 2080 will be 55, 59, and 63 years, respectively, which is longer than the basic Lee-Carter model predicted. The model in this work, unlike the Lee Carter model, is identifiable.

Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy

The State of Health (SOH) of lithium-ion batteries is crucial for maintaining their safety and reliability. Electrochemical impedance spectroscopy (EIS) provides extensive information about the battery state, but the data across high-, medium-, and low-frequency ranges are not independent. Practical challenges in obtaining full-frequency EIS data include long measurement times, low accuracy due to noise, and high costs. This paper proposes an SOH estimation method based on offline EIS and domain-adversarial neural network (DaNN) transfer. Initially, we use a feature search strategy on experimental EIS data to identify the optimal impedance feature subset through the root mean square error (RMSE) and comprehensive indicators. These features are then input into the DaNN for SOH estimation using transfer algorithms. Finally, based on feature correlation analysis, weights are allocated using a Gaussian process regression model for a secondary prediction, optimizing the SOH results. Experiments on laboratory calendar aging and publicly available cycling aging datasets demonstrate the method's effectiveness, achieving an RMSE of <1 %. The proposed method significantly improves SOH estimation accuracy and efficiency, addressing the limitations of traditional methods and reducing dependency on extensive measurement data. This advancement enhances the safety, reliability, and cost-effectiveness of battery applications.

Impact of high-temperature storage on capacity fading of Ni-rich cathodes in sulfide-based all-solid-state batteries

Despite the extensive research efforts focused on enhancing the lifespan and energy density of all-solid-state batteries, the calendar aging behavior of cathodes within the all-solid-state batteries system continues to be underexplored. In this study, we elucidate the capacity fading of Ni-rich cathodes in sulfide-based all-solid-state batteries after high-temperature storage using electrochemical techniques, synchrotron X-ray-based characterization tools, and electron microscopy. High-temperature storage negatively affects the electrochemical performance of Ni-rich cathodes, limiting their charge and discharge capacities, notably charge fading. The underlying cause of capacity fading lies in interfacial side reactions between Ni-rich cathode materials and solid electrolytes. The LiNbO3 coating layer is found to be effective in preventing interfacial degradation, which virtually eliminates charge and discharge fading. These findings offer a deeper understanding of the capacity fading of Ni-rich cathodes in sulfide-based all-solid-state battery systems during high-temperature storage. Furthermore, they provide insights for achieving all-solid-state batteries with high thermal durability and energy density.

SoC Profile Reconstruction in Residential EV Charging: Assessing the Degradation of V1G and V2G in Denmark

The development of Smart Charging (SC) strategies for Electric Vehicles (EVs) is on the rise, driven by the potential benefits for both customers and grid operators. While several studies have explored the impact of these strategies on battery degradation, there is a lack of studies based on real-world data. To overcome the limited information provided by the current EV-EVSE (EV Supply Equipment) communication protocols, a methodology to reconstruct State of Charge (SoC) profiles based on the charging power is proposed and used to reconstruct the profiles and analyse the battery degradation for three residential EVSEs in Denmark, comparing degradation trends in under Uncontrolled Charging (UC) mode, with the SC ones. Results show that the economic profits from unidirectional (V1G) SC strategies based on price, offer significant cost savings (10-22% annually) and reduce calendar ageing by up to 4% over a decade. V2G strategies entail modest increases in cost savings at the cost of a minimal additional degradation.

Extending an Empirical Degradation Model for Li-Ion Batteries into Second-Life Applications

Lithium-ion batteries have been used increasingly in recent years for a wide range of applications, e.g., electric vehicles and large-scale power grids. Lithium-ion batteries suffer from degradation caused by their operation and their exposure to environmental conditions and thus have a limited lifetime within their first application. These batteries might be available for re-use in a different application, commonly under more moderate usage conditions. The influence of such a change in operating conditions on battery degradation within a second life application is not very well understood. Here we present the extension of an empirical degradation model that formulates the degradation process as a function of battery operations for both, first- and second life applications.Our extended semi-empirical model for battery degradation is based on work of Xu et al. [1] and combines the degradation mechanism due to the growth of the SEI layer and the degradation depth obtained in dedicated ageing tests. Within this work we focus on a commercial 64 Ah NMC-graphite pouch cell. The model parametrization is based on a large dataset containing both calendar and cycle life data of more than 100 cells, covering a large window of temperatures (5-45 °C), C-rates (0.2-1.5 C), SoC windows, and average SoC.Figure 1(a) shows the variations of state of health (SoH) with the number of the full equivalent cycle (FEC) for four cells cycling at the same constant ageing conditions (0.75C, 25°C, and 0-100% SoC). Figure 1(b) shows the fitting results of the empirical degradation model using the data from the ageing tests. It is assumed that the SEI layer model parameters are constant for this specific cell. The degradation depth increases linearly with cycle number, where the degradation rate varies with the battery operation condition. The degradation of the battery cell depends not only on external stress factors such as temperature, SoC, DoD, C-rate, but also its previous ageing history. The formation of the SEI film contributes non-linearly to the battery degradation, while the contribution of the degradation rate of different stress factors can be accounted for linearly. The degradation rate depends on the ageing modes: calendar or cycle ageing. Calendar ageing reflects the inherent degradation over time and the rate is affected by temperature and SoC. The calendar degradation rate per time unit can be obtained as a function of temperature and SoC [2, 3]. The cycle SoC window can be interpreted using DoD and mean SoC. The cycle degradation rate per FEC can be estimated as a function of temperature, mean SoC, and DoD [3]. Data-driven models and empirical correlations are applied to obtain battery degradation rate functions. The total degradation depth can be estimated by summarizing the degradation caused by the calendar ageing and the degradation resulting from all cycles.To verify the performance of the empirical degradation model, a dynamic ageing test was designed, including both calendar and cycle ageing. The historical data of SoC and temperature are shown in Figures 2 (a) and 2(b), respectively. Through analyzing the time series data of cell SoC, all cycle information can be obtained to calculate the cycle ageing. The calendar ageing was calculated based on time duration for each cycle, and the average temperature and SoC. Using the correlations for the degradation rates, the degradation depth during for each cycle can be evaluated. The accumulated degradation depth can be used to estimate the change of remaining capacity with time. Figure 2(c) shows the comparison of the prediction of SoH with the experimental data.When moving into the 2nd life regime, degradation mechanisms and their interplay become more complex, leading to a larger statistical variation in ageing rates between individual cells (i.e., see time offset in Fig 2c). This can be accounted for by readjustment of the model’s initial 2nd life SoH to the last SoH from the first life application data, as well as including statistical variation into the model’s parameters.

Long-Term Calendar Aging across Commercial Lithium-Ion Cell Chemistries - Part II: Modeling and Early Prediction

Lithium-ion (Li-ion) batteries are widely used in applications such as mobility, stationary grid systems, and various consumer and commercial systems. In electric vehicles (EVs), batteries often remain idle, with only about 10% utilization. Moreover, in stationary battery energy storage systems (BESS) designed for peak shaving, resting periods tend to cause more aging effects than the operational cycles. Consequently, quantifying the effect of calendar aging is crucial. However, current calendar aging models are inadequate for accurately modeling and predicting the diverse aging behaviors of commercial Li-ion batteries. In this study, we analyze a new, long-term Li-ion battery calendar aging dataset, which includes eight different commercial battery types. We compare and validate three calendar aging models—semi-empirical, symbolic regression, and Long Short-Term Memory Network (LSTM). We examine the transferability of these models across various commercial cell types, offering insights into their generalizability. Additionally, we introduce a novel trajectory-to-trajectory approach for early prediction of calendar aging lifetime. For the first time, we investigate the model's ability to interpolate and extrapolate aging behavior under different storage temperatures. This research provides practical insights for optimizing battery management strategies in conditions dominated by calendar aging.

Long-Term Calendar Aging across Commercial Lithium-Ion Cell Chemistries- Part I – Exploratory Data Analysis

In this study, calendar aging is investigated across different cell chemistries, temperatures, and state of charges in a dataset spanning over 10 years. By tracking the capacity and resistance over the course of degradation, we analyze the similarities and difference in calendar aging seen across chemistries and testing conditions. We parameterize degradation curves using standard semi-empirical models to assess Arrhenius dependence, t^x fitting, and time dependent non-linearity of calendar aging across different chemistries, testing conditions, and cell to cell variability. Through this process we demonstrate the complexity of calendar aging and the difficulty of its modeling, calling for the need for more robust prediction models.

On-Board Diagnostics for Li-Ion Batteries Using Electrochemical Impedance Spectroscopy (EIS)-Based Health Estimation Models

There exists a growing need for standardized On-Board Diagnostics (OBD) for electric vehicles [1] to provide accurate health metrics and guarantees to both consumers and manufacturers.Previous work has shown the powerful LIB capacity-based State-of-Health (SoH) estimation capability of Electrochemical Impedance Spectroscopy (EIS) measurements and data-driven models [2] [3]. Since EIS measurements are dependent not only on SoH but also State-of-Charge (SoC) and temperature [4], it is important that the measurements are conducted after the cell reaches an equilibrium, and that these other variables are also tracked. Although EIS measurements are somewhat quicker than some traditional capacity-determination experimental methods, the time taken for such measurements is not insignificant. Therefore, building a pipeline to determin the EIS frequency measurements most important for SoH estimation is an important task in developing a suitable EIS-based OBD. By exploring a frequency range between 0.1 and 200 Hz, we study EIS measurements related to diffusion and charge transfer processes in LIB operation [5], with each process being partially distinguishable due to their varying timescales.In this work, using EIS data collected from 5Ah LIBs with an NMC-111 cathode and a graphite anode at various SoCs (0, 25, 50, 75 and 100%) and cell lifetime (0, 10, 20, 40 and 90 days) as input features, we develop sequential, data-driven health estimation models for LIBs. The 22 cells used for this analysis have been aged over a period of 90 days in two different ways: either through active cycling at different C-rates (0.2 and 1C) and temperatures (0, 25 and 40⁰C), or passive “calendar aging” where the cells are left without use at a specific temperature and SOC. Using feature attribution techniques (Shapley values, feature occlusion, etc.), we find the most influential of the frequency ranges in the EIS measurements that relate strongly with cell performance degradation. To develop a streamlined and efficient SoH estimation framework, we formulate an optimization problem to find the EIS experimental design in terms of frequency ranges that delivers maximum accuracy in SoH estimation at different points in the lifetime of the cell. These streamlined and optimized EIS experimental designs and SoH estimation models can be used directly in the development of rapid and efficient on-board diagnostic tools.

Impact of High-Temperature Storage on Capacity Fade of Ni-Rich Cathodes in All-Solid-State Batteries

Despite the extensive research efforts focused on enhancing the lifespan and energy density of all-solid-state batteries, the calendar aging behavior of cathode material within the all-solid-state batteries system continues to be underexplored. In this study, we elucidate the capacity fade behavior of Ni-rich cathodes in all-solid-state batteries after high-temperature storage using electrochemical techniques, synchrotron X-ray-based characterization tools, and electron microscopy. High-temperature storage degrades the electrochemical performance of the Ni-rich cathode, constraining the capacity for charging and discharging the electrode. A larger portion of capacity loss is attributed to extensive charge fading, resulting from structural reconstruction on the cathode surface during high-temperature storage. The application of a LiNbO3 coating layer effectively inhibits the interfacial degradation, virtually eliminating both types of fading. These findings offer a deeper understanding of the storage-induced capacity fades of Ni-rich cathodes in ASSBs.

Benefit to Harm (B2H) Ratio for Bulk Vehicle to Grid (V2G) Services Using Lifetime Degradation Digital Twin: Emulating Various Dominant Degradation Mechanism

Vehicle-to-grid (V2G) services, utilizing electric vehicle (EV) batteries for grid support, enhance reliability and reduce peak energy demand. We present a physics-based model tuned for three different cell chemistry families and examine the influence of bulk V2G services on EV battery life. We consider various duty cycles, cell chemistries with nickel content (NMCxyz and various anode graphites), and associated dominant degradation mechanisms. To quantify the impact of offering V2G services, we introduce the V2G benefit-to-harm ratio (B2H). We define the benefit as the Ah gained with V2G and the harm as the life lost in days due to V2G, both compared to the baseline noV2G.B2H=(normalized Ah gained by 70% capacity fade) divided by (time in days reduction by the time that capacity fade has reached 70%). Note that the 70% capacity fade can be changed for various vehicle models depending on the warranty definition of the % capacity or usable battery energy (UBE) at the end of the warranty.Our findings indicate that the dominant degradation mechanism plays a crucial role in the benefit-to-harm (B2H) ratio of V2G or vehicle-to-building (V2B) [1]. Specifically, we clarify that the relative contribution of calendar aging to overall capacity loss is pivotal in determining the degradation due to V2G and possible associated benefits.Our model takes into account four degradation mechanisms based on a single-particle model. SEI growth and cathode transition metal dissolution are part of the calendar aging loss of lithium inventory (LLICal). The remaining LLI is attributed to Li-plating and mechanical degradation caused by particle cracking.We calibrate the model for three families of cell chemistries and conditions. The primary degradation mechanism for one cell family is anode mechanical degradation (LAMNeg). The second family of cells predominantly ages due to SEI layer growth, and the third family is degrading due to both mechanisms [2].We show that, in cases with a higher contribution of calendar aging to degradation (LLICal/LLI), V2G can be potentially more beneficial (in Ah gained by 70% capacity fade) than harmful (lifetime reduction in days by the time that capacity fade has reached 70%). Furthermore, we have evidence that the V2G charging pattern can have a secondary effect on the B2H ratio. For example, being a risk taker and charging the battery late or being cautious and charging it as soon as possible can lead to different degrees of degradation depending on the chemistry and conditions of the battery [3].With our multiphysics reduced order model of battery lifetime degradation, we fully explain why previous studies [4] have shown that the impact on EV battery degradation varies from inconsequential [5] to projecting a need for early battery replacement [6]. Our results clarify that battery chemistry and usage patterns are important factors in determining whether or not to utilize a vehicle for grid support, as well as the overall financial impact on the owner and OEM warranty.

The Voltage-Based Implementation of the Tunneling Mechanism in Aging Models for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have become one of the most crucial power sources for electronics and electric vehicles due to high energy density, long cycle life, environmental friendliness, etc. Precise explanation and prediction of the aging behavior of LIBs is quickly becoming a hotspot. A solid electrolyte interface (SEI) growth is regarded as the dominant factor of capacity losses in LIBs. However, the previous SEI growth and aging models were based on energy levels. That means the energy barrier related to the Fermi level of SEI and electrode was used as the input parameter. It is not straightforward to implement, especially for industrial applications. Furthermore, the state-of-charge (SoC) and the energy barrier were usually assumed to be constant during (dis)charging, contradicting common sense and affecting the calculations' accuracy.The present work proposes and experimentally validates an advanced voltage-based aging model. The voltage of the anode electrode is adopted as an input parameter in this model. It is easier to introduce dependence of aging on the voltage/SoC of the electrode with this model. It is found that deep delithiation of the anodes and lower SoC of LIBs will mitigate electron tunneling, lower SEI growth, and reduce calendar aging. Commonly used anode materials, such as graphite, Li4Ti5O12, and blended Si/C, are presented as interesting cases to investigate the effects of different electrode materials on the tunneling current profiles during (dis)charging for the first time. The model is instructive and can predict aging in LIBs with various electrode materials. This model and related analyses are beneficial for further optimizing LIB's electronic and electrode properties and as interesting limiting cases for testing battery modeling software.

Genetics of Cardiac Aging Implicate Organ-Specific Variation.

Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging. In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics. We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near TTN, TNS1, LSM3, PALLD, DSP, PLEC, ANKRD1 and MYO18B) and an additional 16 loci (near MECOM, NPR3, KLHL3, HDGFL1, CDKN1A, ELN, SLC25A37, PI15, AP3M1, HMGA2, ADPRHL1, PGAP3, WNT9B, UHRF1 and DOK5). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality. These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.

Self-Discharge and Calendar Aging Behavior of Li-Ion and Na-Ion Cells

The calendar aging and self-discharge behavior of Na-Ion cells containing a layered oxide NaNi1/3Fe1/3Mn1/3 (NFM) cathode were investigated and compared to two Li-Ion cell chemistries, G/LiFePO4 (LFP) and SiG/LiNi0.8Mn0.1Co0.1O2 (NMC811). The self-discharge measurements were performed via voltage hold experiments at different states of charge (10%, 40%, 50%, 70%, 90%, and 100%) and temperatures (25 °C, 40 °C and 55 °C). A high-precision coulometry analysis was conducted to investigate the coulombic efficiency (CE), differential voltage analysis (DVA), and end-point slippage. The results show that the Na-Ion cells present a similar self-discharge behavior to the NMC811 Li-Ion cells. In addition, via CE and end-point slippage analysis, strong reversible reactions were observed for the Na-Ion cells. Despite the poor CE values, the cells presented a low capacity loss. Post-mortem analysis showed sodium plating on the edges of all the SOCs investigated. The LFP results presented mainly calendar losses from lithium inventory loss with almost no cathode-related degradation. At high SOCs, both transition metal cathodes, NMC811 Li-Ion and NFM Na-Ion, exhibited more cathode-related processes dominating the self-discharge current and presumably improving the capacity retention due to electrolyte oxidation. Finally, the Na-Ion cells showed anode overhang equalization effects like Li-Ion cells.

Influence of Vinylene Carbonate and Fluoroethylene Carbonate on Open Circuit and Floating SoC Calendar Aging of Lithium-Ion Batteries

The purpose of this study was to investigate the calendar aging of lithium-ion batteries by using both open circuit and floating current measurements. Existing degradation studies usually focus on commercial cells. The initial electrolyte composition and formation protocol for these cells is often unknown. This study investigates the role of electrolyte additives, specifically, vinylene carbonate (VC) and fluoroethylene carbonate (FEC), in the aging process of lithium-ion batteries. The results showed that self-discharge plays a significant role in determining the severity of aging for cells without additives. Interestingly, the aging was less severe for the cells without additives as they deviated more from their original storage state of charge. It was also observed that the addition of VC and FEC had an effect on the formation and stability of the solid electrolyte interphase (SEI) layer on the surface of the carbonaceous anode. By gaining a better understanding of the aging processes and the effects of different electrolyte additives, we can improve the safety and durability of lithium-ion batteries, which is critical for their widespread adoption in various applications.

Capacity Degradation Assessment of Lithium-Ion Battery Considering Coupling Effects of Calendar and Cycling Aging

Capacity degradation of lithium-ion batteries largely determines the cost, performance and environmental impact of various products such as renewable energy production systems, portable electronics, and electric vehicles. Degradation assessment is thus of great importance for prognostic and health management of batteries, which ensures a stable production and operation. There are typically two leading sources contributing to the degradation of a lithium-ion battery, namely, cycling aging during charge/discharge cycles and calendar aging during idle states. However, most existing studies on degradation assessment either only consider a single source or ignore the coupling of these two sources, which makes the evaluation inefficient. A common observation in practice is that a battery tends to degrade faster when it experiences charge/discharge cycles continuously for a longer time. To capture this feature, this paper introduces the concept of the cumulative uninterrupted cycling duration (CUCD), which is defined as the consecutive operation duration since a battery ends the idle state. The CUCD can also help to model the coupling between the calendar and cycling aging. This paper then proposes to model the drift rate of cycling aging as a function of the CUCD and the functional form is determined with the monotonic spline. We estimate the model parameters by maximizing the likelihood function. Hypothesis testings toward the significance of the coupling between two aging sources and the monotonicity of the drift rate function under cycling aging are also provided. The effectiveness and the superiority of the model are validated using numerical and real case studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study aims to characterize the degradation of lithium-ion battery simultaneously considering calendar and cycling agings and the coupling effect. Unlike previous research that only supplied experimental results to qualitatively illustrate the existence of the coupling effect between these two aging sources, this study provides procedures to quantitatively test the significance of such an effect. In addition, previous research ignored the coupling effect in the degradation modeling as it is difficult to capture, this study propose the concept of CUCD to characterize it. The CUCD is also introduced as a stress factor for cycling aging, which is based on the fact that a battery will degrade faster when it experiences charge/discharge cycle continuously for a longer time. Hypothesis testing is also provided to illustrate this monotonicity. To deal with another challenge that the expert knowledge of the functional form between cycling aging and CUCD is unavailable except for the monotonicity, a nonparametric method with the shape-restricted spline is adapted. The estimation procedures for the model parameters are also presented.

Accounting calendar and cyclic ageing factors in diagnostic and prognostic models of second-life EV batteries application in energy storage systems

The rapid expansion of the electric vehicle market has significantly increased the demand for lithium-ion batteries, posing challenges for manufacturers and policymakers regarding efficient use and recycling. When these batteries reach the end of their primary lifecycle, their repurposing for secondary applications such as energy storage becomes critical to addressing environmental and resource management issues. This paper focuses on applying second-life batteries in energy storage systems, emphasizing the importance of accounting for calendar and cyclic aging factors to optimize battery performance and longevity. Calendar aging refers to the degradation that occurs over time due to chemical reactions within the battery, even when it is not in use. This type of aging is influenced by temperature, state of charge (SOC), and storage conditions. Cyclic aging, on the other hand, results from repeated charging and discharging cycles, which cause mechanical and chemical changes within the battery, leading to capacity fade and increased internal resistance. The combined effects of these aging processes necessitate the development of high-precision diagnostic and prognostic models to manage the performance and longevity of second-life batteries effectively. In Ukraine, the adoption of electric vehicles is accelerating, leading to an influx of used electric vehicles. This situation necessitates the prompt development of strategies for repurposing these batteries for energy storage applications. The complexities associated with final recycling processes make secondary use an attractive interim solution. By repurposing used EV batteries, Ukraine can mitigate immediate challenges related to battery waste and resource scarcity while supporting the transition to renewable energy sources. This paper highlights the need for an integral degradation index (DI) that combines calendar and cyclic aging factors with stochastic influences to provide a comprehensive measure of battery health. Such an index is essential for optimizing battery management practices, including the scheduling of charging and discharging cycles, to extend the operational life of secondary batteries. The study also presents practical recommendations for implementing these models in various energy storage scenarios, ranging from residential solar energy systems to industrial grid support and electric vehicle charging stations. By adopting optimized battery management strategies, the potential for extending the lifespan of secondary batteries and reducing operational costs is significant. This approach supports sustainable energy practices and aligns with global efforts to promote renewable energy sources and circular economy principles. Keywords: Lithium-Ion Battery, Electric Vehicle, Energy Storage, Battery Degradation, Calendar Ageing, Cyclic Ageing, Integral Degradation Index, Remaining Useful Life, State of Health.

New methods in dendroarchaeology: possibilities for studying charcoals from archaeological sites (the case of the Kara-Dyt II site, Republic of Tyva)

Traditionally, charcoal from archaeological sites was considered an unsuitable material for dendrochronological dating. The authors examined 23 coal samples from the Kara-Dyt II site (Republic of Tyva, Russia). An analysis of existing solutions for sample preparation of such material showed their low speed and efficiency. This article uses the approach developed by the authors, which allows for quick and high-quality sample preparation of a large number of charcoals with minimal labour costs. As a result, it was possible to construct a 138-year tree-ring chronology using archaeological charcoal. Further work in this direction will make it possible to solve the problem of constructing a long-term tree-ring chronology in the arid zone of South Siberia. This chronology provides a unique tool for determining the calendar age of wood from numerous burial mounds in Tuva and for an in-depth study of the relationship between climate and society over the past two millennia.

Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems

Batteries play a crucial role in the domain of energy storage systems and electric vehicles by enabling energy resilience, promoting renewable integration, and driving the advancement of eco-friendly mobility. However, the degradation of batteries over time remains a significant challenge. This paper presents a comprehensive review aimed at investigating the intricate phenomenon of battery degradation within the realm of sustainable energy storage systems and electric vehicles (EVs). This review consolidates current knowledge on the diverse array of factors influencing battery degradation mechanisms, encompassing thermal stresses, cycling patterns, chemical reactions, and environmental conditions. The key degradation factors of lithium-ion batteries such as electrolyte breakdown, cycling, temperature, calendar aging, and depth of discharge are thoroughly discussed. Along with the key degradation factor, the impacts of these factors on lithium-ion batteries including capacity fade, reduction in energy density, increase in internal resistance, and reduction in overall efficiency have also been highlighted throughout the paper. Additionally, the data-driven approaches of battery degradation estimation have taken into consideration. Furthermore, this paper delves into the multifaceted impacts of battery degradation on the performance, longevity, and overall sustainability of energy storage systems and EVs. Finally, the main drawbacks, issues and challenges related to the lifespan of batteries are addressed. Recommendations, best practices, and future directions are also provided to overcome the battery degradation issues towards sustainable energy storage system.

Electrochemical instability of room-temperature ionic liquids with LiTFSI at elevated temperature and its consequences in Li/Li-ion based half-cells and full-cells

High-temperature Li-ion batteries capable of operating up to 100 ℃ can replace lithium thionyl chloride primary batteries in specialized industrial applications. Room-temperature ionic liquids (RTIL) offer high thermal stability and can be a promising choice of electrolyte for high-temperature batteries. This work elucidates that high thermal stability alone does not guarantee effective battery operation due to the electrochemical instability observed in RTIL-LiTFSI mixtures at elevated temperatures. Here, we investigate the impact of varying electrochemical stability in RTILs on half-cell, full-cell, and calendar aging. Electrochemical instability of the electrolyte at the cathode interface injects additional electrons and Li-ions into the electrochemical cell, triggering a cascade of detrimental chain reactions that hinder efficient battery performance. We demonstrate that electrochemical information gathered from a full-cell configuration can frequently obscure the presence of electrochemical instability at the cathode surface. To accurately evaluate this instability, it is essential to perform in-depth half-cell studies and other characterizations. Our results indicate that cathode electrolyte interphase (CEI) can reach a thickness of up to 100 nm, with a depth-dependent composition showing higher concentration of inorganic species like LiF and LiNSO near the cathode surface. Further, accelerated calendar aging measurements of such cells at high temperature revealed complete irreversible self-discharge within as little as 14 days. These findings shed light on the critical factors influencing the stability and performance of cells under challenging operating conditions.