Search Results (5,471)

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs. Full article

Background/Objectives: Australian terrestrial mammals that fall within the critical weight range (35 g–5.5 kg) have experienced large population declines due to a combination of habitat loss and modification, and the introduction of non-native cats, dogs, and foxes. Because running speed typically increases with body size, predators are usually faster but less agile than their prey due to the biomechanical trade-offs between speed and agility. Quantifying the maximum locomotor capacities of Australian mammals in the critical weight range, and the magnitude of the trade-off between speed and agility, can aid in estimating species’ vulnerability to predation. Methods: To do this, we quantified the trade-off between speed and agility in both males and females (n = 36) of a critical weight range species, the northern brown bandicoot (Isoodon macrourus), and determined if there was an influence of morphology on locomotor performance. Results: When turning, individuals who had higher turn approach speeds, and higher within-turn speeds, had greater turning radii and lower angular velocities, meaning a decrease in overall maneuverability. Females were more agile and exhibited greater turning speeds at similar turning radii than males. For both sexes, individuals with longer relative hind digits had relatively faster sprint speeds, while those with longer forearms had relatively smaller turning radii and higher agility. Conclusions: Due to the constrained limb morphology of the bandicoot species, these findings could translate across this group to provide a better understanding of their escape performance and risk of predation. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R² and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions. Full article

Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of remote sensing sensors for estimating biophysical variables in Mediterranean and temperate forests that can be easily translated into an operational context. Aboveground biomass (AGB), canopy height (CH), and forest canopy cover (FCC) were estimated using a combination of optical (Sentinel-2, Landsat) and radar sensors (Sentinel-1 and TerraSAR-X/TanDEM-X), along with records of past forest disturbances and topography-related variables. As a reference, lidar-derived AGB, CH, and FCC were used. Model performance was assessed not only with standard approaches such as out-of-bag sampling but also with completely independent lidar-derived reference datasets, thus enabling evaluation of the model’s temporal inference capacity. In Mediterranean forests, models based on optical imagery outperformed the radar-enhanced models when estimating FCC and CH, with elevation and spectral indices being key predictors of forest structure. In contrast, in denser temperate forests, radar data (especially X-band relative heights) were crucial for estimating CH and AGB. Incorporating past disturbance data further improved model accuracy in these denser ecosystems. Overall, this study underscores the value of integrating multi-source remote sensing data while highlighting the limitations of temporal extrapolation. The presented methodology can be adapted to enhance forest variable estimation across many forest ecosystems. Full article

The use of dry powder inhalers (DPIs) represents a cornerstone in the treatment of chronic pulmonary diseases. However, suboptimal inhalation techniques, including inadequate airflow rates, have been a persistent concern for achieving effective therapeutic outcomes, as many patients remain unaware of their insufficient inhalation performance. As an effective strategy, a digital monitoring system, coupled with dry powder inhalers (DPIs), has emerged to estimate flow profiles and provide inhalation information. The estimation could be further facilitated by the application of artificial intelligence (AI). In this work, a novel digital system to primarily monitor pressure during DPI usage was successfully designed, and advanced machine learning (ML) techniques were then employed to estimate inhalation flow profiles based on the captured data. Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. By using these models, inhalation flow profiles could be successfully estimated, with an excellent accuracy of 97.7% for Peak Inspiratory Flow Rate (PIFR) and 95.2% for inspiratory capacity (IC). In summary, the pressure-based digital monitoring system empowered by AI techniques could be successfully applied to assess inhalation flow profiles with excellent accuracy. Full article

Dynamic Line Rating (DLR) technology is presented as a key solution to optimize the transmission capacity of power lines without the need to make investments in new infrastructure. Unlike traditional methods based on static estimates, DLR allows the thermal capacity of conductors to be evaluated in real time, considering the environmental and operational conditions. This article presents a state-of-the-art analysis of this technology, including a review of the main solutions currently available on the market. Likewise, the influence of variables such as ambient temperature, wind speed and direction or solar radiation in the determination of dynamic load capacity is discussed. It also reviews various pilot and commercial projects implemented internationally, evaluating their results and lessons learned. Finally, the main technological, regulatory, and operational challenges faced by the mass adoption of DLR are identified, including aspects such as the prediction of the dynamic capacity value, combination with other flexibility options, or integration with network management systems. This review is intended to serve as a basis for future developments and research in the field. Full article

The inevitable decline in battery performance presents a major barrier to its widespread industrial application. Adaptive and accurate estimation of battery capacity is paramount for battery operation, maintenance, and residual value evaluation. In this paper, we propose a novel battery capacity estimation method based on an approximate open circuit voltage curve. The proposed method is rigorously tested using both lithium–iron–phosphate (LFP) and nickel–cobalt–manganese (NCM) battery packs at multiple charging rates under varied aging conditions. To further enhance capacity estimation accuracy, a voltage correction strategy is implemented utilizing the incremental capacity (IC) curve. This strategy also verifies the potential benefits of increasing the charging rate to shorten the overall test duration. Eventually, the capacity estimation error is consistently controlled within 2%. With optimal state of charge (SOC) interval selection, the estimation error can be further reduced to 1%. Clearly, our proposed method exhibits excellent compatibility across diverse battery materials and degradation states. This adaptability holds substantial scientific value and practical importance. It contributes to the safe and economic utilization of Li-ion batteries throughout their entire lifespan. Full article

As energy storage technology advances rapidly, the power industry demands accurate state estimation of lithium batteries in energy storage power stations. This study aimed to improve such estimations. An improved Transformer structure was employed to estimate the battery’s state of charge (SOC). The Time Delay Second Estimation (TDSE) algorithm optimized the improved Transformer model to overcome traditional models’ limitations in extracting long-term dependency. Innovative particle filter algorithms were proposed to handle the nonlinearity, uncertainty, and dynamic changes in predicting remaining battery life. Results showed that for LiNiMnCoO₂ positive electrode datasets, the model’s max SOC estimation error was 2.68% at 10 °C and 2.15% at 30 °C. For LiFePO₄ positive electrode datasets, the max error was 2.79% at 10 °C (average 1.25%) and 2.35% at 30 °C (average 0.94%). In full lifecycle calculations, the particle filter algorithm predicted battery capacity with 98.34% accuracy and an RMSE of 0.82%. In conclusion, the improved Transformer and TDSE algorithm enable advanced battery state prediction, and the particle filter algorithm effectively predicts remaining battery life, enhancing the adaptability and robustness of lithium battery state analysis and offering technical support for energy storage station management. Full article

To support bearing capacity estimates, this study develops and tests a geoprocessing workflow for predicting soil properties using Empirical Bayesian Kriging 3D and a classification function. The model covers a 183 m × 185 m × 24 m site in Astana (Kazakhstan), based on 16 boreholes (15–24 m deep) and 77 samples. Eight geotechnical properties were mapped in 3D voxel models (812,520 voxels at 1 m × 1 m × 1 m resolution): cohesion (c), friction angle (φ), deformation modulus (E), plasticity index (PI), liquidity index (LI), porosity (e), particle size (PS), and particle size distribution (PSD). Stratification patterns were revealed with ~35% variability. Maximum φ (34.9°), E (36.6 MPa), and PS (1.29 mm) occurred at 8–16 m; c (33.1 kPa) and PSD peaked below 16 m, while PI and e were elevated in the upper and lower strata. Strong correlations emerged in pairs φ-E-PS (0.91) and PI-e (0.95). Classification identified 10 soil types, including one absent in borehole data, indicating the workflow’s capacity to detect hidden lithologies. Predicted fractions of loams (51.99%), sandy loams (22.24%), and sands (25.77%) matched borehole data (52%, 26%, 22%). Adjacency analysis of 2,394,873 voxel pairs showed homogeneous zones in gravel–sandy soils (28%) and stiff loams (21.75%). The workflow accounts for lateral and vertical heterogeneity, reduces subjectivity, and is recommended for digital subsurface 3D mapping and construction design optimization. Full article

The predictive performance of the remaining useful life (RUL) estimation model for bearings is of utmost importance, and the setting method of the bearing degradation threshold is crucial for detecting its early degradation point, as it significantly affects the performance of the RUL prediction model. To solve these problems, a bearing RUL prediction method based on early degradation detection and optimized BiLSTM is proposed: an optimized VMD combined with the Pearson correlation coefficient is used to denoise the bearing signal. Afterward, multi-domain features are extracted and evaluated using different metrics. The optimal degradation feature is then selected. Furthermore, KPCA is used to integrate the features and establish the health indicators (HIs) for early degradation detection of bearings using a sliding window method combined with the 3σ (3-sigma) criterion and the quartile method. The RUL prediction model is developed by combining the BiLSTM network with the attention mechanism and by employing the SSA to adaptively update the network parameters. The proposed RUL prediction model is tested on various datasets to evaluate its generalization ability and applicability. The obtained results demonstrate that the proposed denoising method has high performance. The dynamic 3σ-threshold setting method accurately detects the early degradation points of bearings. The proposed RUL prediction model has high performance and fitting capacity, as well as very high generalization ability and applicability, enabling the early prediction of bearing RUL. Full article

Traditional data-driven approaches to lithium battery state of health (SOH) estimation face the challenges of difficult feature extraction, insufficient prediction accuracy and weak generalization. To address these issues, this study proposes a novel prediction framework with transfer learning-based linear regression (LR) and a convolutional neural network (CNN) under limited data. In this framework, first, variable inertia weight-based improved particle swarm optimization for variational mode decomposition (VIW-PSO-VMD) is proposed to mitigate the volatility of the “capacity resurgence point” and extract its time-series features. Then, the T-Pearson correlation analysis is introduced to comprehensively analyze the correlations between multivariate features and lithium battery SOH data and accurately extract strongly correlated features to learn the common features of lithium batteries. On this basis, a combination model is proposed, applying LR to extract the trend features and combining them with the multivariate strongly correlated features via a CNN. Transfer learning based on temporal feature analysis is used to improve the cross-domain learning capabilities of the model. We conduct case studies on a NASA dataset and the University of Maryland dataset. The results show that the proposed method is effective in improving the lithium battery SOH estimation accuracy under limited data. Full article

Drag embedment anchors (DEAs) provide reliable and cost-effective mooring solutions for floating structures, e.g., platforms, ships, offshore wind turbines, etc., in offshore engineering. Structural stability and operational safety require accurate predictions of their penetration depths and holding capacities across various seabed conditions. In this study, explicit linear regression (LR) models were developed for the first time to predict the penetration depth and holding capacity of DEAs on clay and sand seabed. Buckingham’s theorem was also applied to identify dimensionless groups of parameters that influence DEA behavior, e.g., the penetration depth and holding capacity of the DEAs. LR models were developed and validated against experimental data from the literature for both clay and sand seabed. To evaluate model performance and identify the most accurate LR models to predict DEA behavior, comprehensive sensitivity, error, and uncertainty analyses were performed. Additionally, LR analysis revealed the most influential input parameters impacting penetration depth and holding capacity. Regarding offshore mooring design and geotechnical engineering applications, the proposed LR models offered a practical and efficient approach to estimating DEA performance across various seabed conditions. Full article