Search Results (19)

In consideration of the characteristics of two-stage (stable and degraded), nonlinearity and non-stationary randomness in the full life-cycle evolution process of the rolling bearing health indicator (HI), a novel remaining useful life (RUL) prediction method for rolling bearings is proposed based on long short-term memory network–Mahalanobis distance (LSTM-MD) and an incremental unscented Kalman filter (IUKF). First, an LSTM-MD hybrid algorithm is developed to precisely identify the critical change point (CP) between stable operation and incipient degradation in bearing HI trajectories, effectively mitigating the susceptibility of conventional threshold-based methods to HI fluctuations. Second, during the degradation stage, a degradation analysis model based on the nonlinear Wiener process is constructed. Simultaneously, an IUKF-based RUL prediction method for bearings is proposed, which overcomes the implicit assumption of the traditional UKF method that one-step prediction can replace state prediction, particularly in scenarios with significant HI fluctuations, thereby significantly reducing prediction errors. Finally, the proposed method is validated through comparisons with traditional methods using both the XJTU-SY public dataset and a self-built bearing test dataset. The results demonstrate that compared to traditional methods, the accuracy of initial degradation change point identification is improved by 32.6%, and the root mean square error (MSE) of RUL prediction is decreased by 41.8%. Full article

Accurate State of Health (SOH) estimation is critical for the reliable and safe operation of lithium-ion batteries; this paper proposes an ORIME–Transformer–BILSTM model integrating multiple health factors and achieves high-precision SOH prediction. Traditional single-dimensional health factors (HFs) struggle to predict battery SOH accurately and stably. Therefore, this study employs Spearman and Kendall correlation coefficients to analyze multi-dimensional HFs and determine the key characteristics for quantifying SOH. The self-attention mechanism of the Transformer encoder extracts and fuses the key features of long-term sequences. A BILSTM network receives these input vectors, whose primary function is to uncover the temporal evolution of the SOH. Finally, the optimal random-weight-initialization meta-heuristic estimation (ORIME) algorithm adaptively adjusts the hyperparameters to optimize the model efficiently. Cycle data from four batteries (B5, B6, B7 and B18) provided by NASA are used for testing. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root-mean-square error (RMSE) of the proposed method are 0.2634%, 0.4337% and 0.3106% Compared to recent state-of-the-art methods, this approach significantly reduces prediction errors by 33% to 67%, unequivocally confirming its superiority and robustness. This work provides a highly accurate and generalized solution for SOH estimation in real-world battery management systems. Full article

Electrochemical Impedance Spectroscopy (EIS) measurements are highly sensitive to the fixturing, temperature, and state of charge (SoC) of batteries. For 10 kWh automotive battery modules, we show that variations in SoC and temperature introduce significant errors at low-to-medium frequencies (<100 Hz), while improper fixture wiring affects mainly higher-frequency accuracy, with errors up to 100% in the imaginary part at 1 kHz. In addition, we study repeatability across various tester-module configurations. EIS results remain highly consistent (±100 µΩ) across three different modules. Comparing the same module across two different testers, deviations are even lower (±30 µΩ up to 1 kHz). The EIS evolution is studied with respect to the cycle numbers, where a strong correlation of low-frequency impedance features is demonstrated. A new combined quotient feature is introduced and suggested as a reliable and efficient state of health (SoH) indicator, solely based on a model-free and phenomenological approach. The study demonstrates the potential of EIS as a powerful tool for battery module characterization, provided that its requirements and limitations are carefully addressed through well-defined experimental setups. Accurate and repeatable EIS measurements are particularly important for obtaining accurate electrochemical insights, especially in the low-to-mid frequency domain, where impedance variations are most sensitive to battery states and ageing effects. Full article

This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)₂ on Ni foam—stabilizing α-nanoflakes at 0.7 V·s⁻¹ and β-platelets at 0.007 V·s⁻¹—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm⁻² at 5 mA·cm⁻²) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring. Full article

This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO₄)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge–discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models. Full article

In this work, we employed an electrochemical impedance spectroscopy analysis of commercial Li-ion Panasonic NCR18650B cells in order to monitor their cycle life performance and the influence of the C-rate on the charge/discharge processes. By applying a fast charge rate of 1.5 C, we investigated their speed degradation within three distinct discharge rates, namely, 0.5 C, 1 C, and 1.5 C. In our first approach, we assessed the dynamics of the lithium-ion transport processes, as well as their dependence on discharge rates, with the aim of understanding how their performance correlates with usage conditions. We observed that, as the discharge current increases while the number of cycles decreases, the ohmic resistance in the aged state reduces. Moreover, the charge transfer resistance is not affected by the discharge current, as the values are inversely proportional to the current rate, but mostly by the number of cycles. By performing a state of health analysis of Li-ion batteries with different C-rates until they were completely discharged, we offer a clear indication of how much of the battery’s lifetime available energy was consumed and how much was left, anticipating further issues or when the battery needed replacing. Starting at 60% state of health, the battery degradation has a steeper increase at 0.5 C and 1 C, respectively, while for a deep 1.5 C discharge, it only increases when the battery charge rate can no longer be sustained. Finally, the resonance frequency results highlight a fast increase toward the end of life for 0.5 C and 1 C, which is directly correlated with the above results, as a potentiostatic electrochemical impedance spectroscopy sequence was applied every fourth charge/discharge cycle. When applied at 1.5 C, the linear trend is much more pronounced, similar to the state of health results. Full article

Urban cycling is a sustainable mode of transportation in large cities, and it offers many advantages. It is an eco-friendly means of transport that is accessible to the population and easy to use. Additionally, it is more economical than other means of transportation. Urban cycling is beneficial for physical health and mental well-being. Achieving sustainable mobility and the evolution towards smart cities demands a comprehensive analysis of all the essential aspects that enable their inclusion. Road safety is particularly important, which must be prioritized to ensure safe transportation and reduce the incidence of road accidents. In order to help reduce the number of accidents that urban cyclists are involved in, this work proposes an alternative solution in the form of an intelligent computational assistant that utilizes simplified machine learning (SML) to detect potential risks of unexpected collisions. This technological approach serves as a helpful alternative to the current problem. Through our methodology, we were able to identify the problem involved in the research, design, and development of the solution proposal; collect and analyze data; and obtain preliminary results. These results experimentally demonstrate how the proposed model outperforms most state-of-the-art models that use a metric learning layer for small image sets. Full article

Second-life applications for lithium-ion batteries offer the industry opportunities to defer recycling costs, enhance economic value, and reduce environmental impacts. An accurate prognosis of the remaining useful life (RUL) is essential for ensuring effective second-life operation. Diagnosis is a necessary step for the establishment of a reliable prognosis, based on the aging modes involved in a cell. This paper introduces a method for characterizing specific aging phenomenon in Graphite/Lithium Iron Phosphate (G/LFP) cells. This method aims to identify aging related to the loss of active material at the negative electrode (LAM_NE). The identification and tracking of the state of health (SoH) are based on Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) peak-tracking techniques. The remaining capacity of the electrode is thus evaluated based on these diagnostic results, using a model derived from half-cell electrode characterization. The method is used on a G/LFP cell in the format 18650, with a nominal capacity of 1.1 Ah, aged from its pristine state to 40% of state of health. Full article

A huge increase in fast−charging stations will be necessary for the transition to EVs. Nevertheless, charging a battery pack at a higher C−rate impacts its state of health, accelerating its degradation. The present paper proposes a different and innovative approach that considers the daily routine of an EV Li−ion battery based on a standard driving cycle, including charging phases when the depth of discharge is 90%. Through dynamic modeling of the EV battery system, the state of charge evolution is determined for different charging C−rates, considering both real discharging and charging current profiles. Finally, by applying a suitable post−processing procedure, aging test features are defined, each being related to a specific EV battery working mode, including charging at a particular C−rate, considering the global battery operation during its lifespan. It is demonstrated that, according to the implemented procedure, fast−charging cycles at 50 kW reduce battery lifespan by about 17% with respect to charge in a 22 kW three−phase AC column, in parity with the discharge rate. Thus, this work can provide a deep insight into the expected massive penetration of electric vehicles, providing an estimate of battery useful life based on charging conditions. Full article

Rechargeable lithium-ion batteries are currently the most viable option for energy storage systems in electric vehicle (EV) applications due to their high specific energy, falling costs, and acceptable cycle life. However, accurately predicting the parameters of complex, nonlinear battery systems remains challenging, given diverse aging mechanisms, cell-to-cell variations, and dynamic operating conditions. The states and parameters of batteries are becoming increasingly important in ubiquitous application scenarios, yet our ability to predict cell performance under realistic conditions remains limited. To address the challenge of modelling and predicting the evolution of multiphysics and multiscale battery systems, this study proposes a cloud-based AI-enhanced framework. The framework aims to achieve practical success in the co-estimation of the state of charge (SOC) and state of health (SOH) during the system’s operational lifetime. Self-supervised transformer neural networks offer new opportunities to learn representations of observational data with multiple levels of abstraction and attention mechanisms. Coupling the cloud-edge computing framework with the versatility of deep learning can leverage the predictive ability of exploiting long-range spatio-temporal dependencies across multiple scales. Full article

The coupling cycles of soil carbon (C), nitrogen (N), and phosphorus (P) have a significant impact on biogeochemical processes and ecosystem services. For centuries, large areas of floodplain wetlands in China have been extensively reclaimed for agricultural purposes due to population growth. However, little is known about the evolution of soil C:N:P stoichiometry along a reclamation chronosequence, particularly across different land uses. In this study, we investigated the variations in soil C:N:P ratios with land use and time gradients along a reclamation chronosequence comprising c. 0, 60, 100, 280, 2000, and 3000 years. Land reclamation induced nutrient decoupling, as it facilitated C and N accumulation from biological processes but restricted P supply controlled by geochemical processes. Soil C and N sequestration reached a stable state after 2000 years, while P declined steadily from 60 years. Soil C/P and N/P increased significantly and were controlled by organic carbon (OC) and total nitrogen (TN), respectively, indicating that an increase in C and N could also promote P uptake. Soil C/N declined in the first 60 years and stabilized at a threshold of 10:1. Different land use patterns following reclamation resulted in distinct soil nutrient structures. Paddies retained more OC and TN but exhibited lower adsorption of total phosphorus (TP) compared to adjacent dryland, leading to significant differences in C/P and N/P between land uses. Based on the redundancy analysis and random forest model, soil OC and TN were mainly affected by the abundance of bacteria metabolizing cellulose, while metal oxides, including Fe₂O₃ and CaO, could best predict TP. Soil C/P and N/P were mainly driven by soil texture and rose significantly with the increasing proportion of clay particles. Our study suggests that as reclamation proceeds, more anthropogenic management is required to regulate potential nutrient imbalances in order to prevent adverse effects on crop growth, soil quality, and ecosystem health. Additionally, any fertilization strategy should be developed based on dryland C and N deficiencies, and lack of P in paddies. Full article

The high-rate discharging performance of lithium titanate batteries is a crucial aspect of their functionality. Under high-power demands, the discharge rate, which is defined as the ratio of discharge current to the maximum capacity, can exceed 50 C or higher. This study investigates the evolution of incremental capacity (IC) curves and frequency response characteristic of 2 Ah lithium titanate batteries subjected to aging cycles at 50 C. The results provide a new indicator to assess the fading of the state of health (SOH) of lithium titanate batteries during ultra-high-rate discharge cycles. Full article

The world has been facing the COVID-19 pandemic, which has come with an unprecedented impact on general physical health and financial and social repercussions. The adopted mitigation measures also present significant challenges to the population’s mental health and health-related programs. It is complex for public organizations to measure the population’s mental health to incorporate its feedback into their decision-making process. A significant portion of the population has turned to social media to express the details of their daily life, making these public data a rich field for understanding emotional and mental well-being. To this end, by using open sentiment analysis tools, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies. Several modern language models were evaluated and compared using intrinsic and extrinsic tasks, that is, the sentiment analysis evaluation of public domain tweets related to the COVID-19 pandemic in Mexico. This study provides a fair evaluation of state-of-the-art language models, such as BERT and VADER, showcasing their metrics and comparing their performance against a real-world task. Results show the importance of selecting the correct language model for large projects such as this one, for there is a need to balance costs with the model’s performance. Full article

Background: The present work starts from a literature review of the evolution of Integrated Management Systems (IMSs), considering different points of view and standards: quality, environmental, occupational health and safety, sustainability and social issues. Even if the benefits are possible, there is not a common approach and a clear link between the integration of management systems and business performance, in particular considering safety performance. Methods: The present study analyzes the application of Risk Assessment in order to realize the integration of management systems. The main objective is to provide a tool for an integrated evaluation of all company performances, starting from the definition of some Key Performance Indicators—KPIs—proposed for a particular case study, even if their choice is not the core of the paper. The assessment team members on the basis of their knowledge, experience and useful literature, could choose the right KPIs for the specific application, able to take a picture of the current state and to suggest a possible recommended action of improving. The proposed Risk Assessment approach is an integration of modern management techniques: Integrated Management System and Improving Cycle DMAIC. Results: The new method, called the Global Performance Index for Integrated Management System—GPI-IMS, has been applied to a real case study in the logistic field in order to evaluate its goodness and possible generalization. Conclusions: The proposed method allows to define the requirements that any company must have to perform the best. The role of the assessment team is very important to evaluate the global performance of the company and to suggest the corrective actions to be adopted. Full article

The interest in employing absorption refrigeration systems is usually related to electricity’s precariousness since these systems generally use thermal rejects for their activation. The application of these systems is closely linked to the concept of energy polygeneration, in which the energy demand to operate them is reduced, which represents their main advantage over the conventional vapor compression system. Currently, the solution pairs used in commercial absorption chillers are lithium bromide/water and ammonia/water. The latter pair has been used in air conditioning and industrial processes due to the ammonia operation’s low temperature. Few review papers on absorption chillers have been published, discussing the use of solar energy as the input source of the systems, the evolution of the absorption refrigeration cycles over the last decades, and promising alternatives to increase the performance of absorption refrigeration systems. There is a lack of consistent studies about designing requirements for absorption chillers, so an updated review covering recent advances and suggested solutions to improve the use and operation of those absorption refrigeration systems using different working fluids is relevant. Hence, this presents a review of the state-of-the-art of ammonia/absorbent based absorption refrigeration systems, considering the most relevant studies, describing the development of this equipment over the years. The most relevant studies in the open literature were collected to describe this equipment’s development over the years, including thermodynamic properties, commercial manufacturers, experimental and numerical studies, and the prototypes designed and tested in this area. The manuscript focuses on reviewing studies in absorption refrigeration systems that use ammonia and absorbents, such as water, lithium nitrate, and lithium nitrate plus water. As a horizon to the future, the uses of absorption systems should be rising due to the increasing values of the electricity, and the environmental impact of the synthetic refrigerant fluids used in mechanical refrigeration equipment. In this context, the idea for a new configuration absorption chiller is to be more efficient, pollutant free to the environment, activated by a heat substantiable source, such as solar, with low cost and compactness structure to attend the thermal needs (comfort thermal) for residences, private and public buildings, and even the industrial and health building sector (thermal processes). To conclude, future recommendations are presented to deal with the improvement of the refrigeration absorption chiller by using solar energy, alternative fluids, multiple-effects, and advanced and hybrid configurations to reach the best absorption chiller to attend to the thermal needs of the residential and industrial sector around the world. Full article