Search Results (832)

The increasing use of silver nanoparticles (AgNPs) as nanoagrochemicals raises important environmental and toxicological considerations of their usage. AgNPs influence soil microbiome functioning, which regulates essential nutrient availability. However, their effects on key chemical soil health indicators remain unclear, with existing studies limited to concentrations ≥10-fold above predicted environmental levels. The aim of the work was to evaluate the effect of AgNPs at a realistic concentration of 10 μg/kg on the principal chemical soil health indicators, including acidity, redox potential, electrical conductivity, contents of NPK, and soil organic carbon (SOC). In addition, dissolved organic carbon and nitrogen (DOC and DON) and water-extractable elements (Al, Ca, Fe, K, Mg, Na, P, S, and Si) were also examined. The laboratory experiment was carried out for 3 months on Retisol, Chernozem, and Solonetz. AgNPs stabilised with carboxymethylcellulose (AgNP-CMC) or polyvinylpyrrolidone (AgNP-PVP) were used. AgNP-induced changes exhibited non-monotonic patterns, peaking at 2–3 months of incubation. A statistically significant effect observed across all soils following AgNPs application included only increased water-extractable Fe. In addition, AgNPs increased nitrate content 1.1–1.4-fold in Retisol and Chernozem, while available phosphorus increased 1.4-fold in Solonetz. However, changes were transient, indicating no pronounced long-term impact on soil properties. Partial Least Square (PLS) analysis revealed that chemical soil health indicators and water-extractable elements do not reliably discriminate between control soils and soils amended with AgNPs. Although our study shows that AgNPs had neither markedly negative nor positive effects on chemical soil health indicators or water-extractable element contents, future research should prioritise field trials. Model experiments under optimised microbial activity conditions limit direct extrapolation to field scenarios. Full article

Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

Accurate battery state-of-charge (SOC) estimation is a core function of battery management systems (BMSs) for electric vehicles (EVs), as it directly affects energy management, safety, and reliability. However, battery aging significantly degrades the accuracy of conventional SOC estimation methods by causing capacity loss, increased internal resistance, and changes in voltage response characteristics. To address these issues, this study proposes an aging-aware SOC estimation method that combines an equivalent-circuit model (ECM) with an extended Kalman filter (EKF). In the proposed framework, aging effects are explicitly incorporated by using offline-identified SOH-dependent model parameters, including effective capacity, RC parameters, and SOC–OCV characteristics, and scheduling these parameters within the EKF prediction and correction process according to the available SOH information. Furthermore, the performance of the proposed method is experimentally validated under an Urban Dynamometer Driving Schedule (UDDS) using cylindrical lithium-ion cells with large current fluctuations. The experimental results demonstrate that the proposed aging-aware EKF maintains stable SOC estimation performance not only in the initial battery state but also throughout the gradual aging process and up to the end of battery life. These results demonstrate the potential of SOH-scheduled, aging-aware EKF-based SOC estimation to improve SOC accuracy in aged batteries under the investigated laboratory and dynamic load conditions. Full article

Accurate prediction of soil organic carbon (SOC) in cropland is important for food production, sustainable soil management, and carbon sequestration. Although digital soil mapping (DSM) has been widely used in the prediction of SOC, most of the current DSM studies use only a single remote sensing image and a single machine learning (ML) approach, and few studies apply multi-temporal remote sensing images and ensemble methods. This study explores the accuracy of the prediction of surface SOC in cropland by comparing multi-temporal Sentinel-2A remote sensing with random forest (RF), support vector machine (SVM), gradient boosted decision trees (GBDT), extreme gradient boosted decision trees (XGBoost), and a stacking ensemble method consisting of these four ML approaches. The potential of multi-temporal remote sensing data and the stacking ensemble method for SOC prediction is discussed. To this end, 76 sampling points were selected in the study area, soil samples were collected at depths of 0–10 cm and 10–20 cm for each soil profile, and a total of 152 soil samples were obtained. Remote sensing variables extracted from topography, climate, and Sentinel-2A images on 13 January and 31 August 2023 were used as predictor variables. The results showed that the stacking ensemble method with multi-temporal predictor variables outperformed all single models and variable combinations. However, the overall predictive accuracy remained moderate, with the best performance for 0–10 cm (R² = 0.386, RMSE = 4.782, MAE = 3.36) and 10–20 cm (R² = 0.425, RMSE = 4.484, MAE = 4.031). The relatively low R² values, despite the use of advanced methods, highlight the inherent challenges of SOC prediction in highly fragmented karst croplands. This study demonstrates the incremental benefit, rather than a universal high accuracy, of combining multi-temporal Sentinel-2 imagery with a stacking ensemble to improve SOC mapping in such complex environments. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article

Wind-storage coordinated frequency regulation enhances the frequency stability of large-scale wind power systems. However, existing methods often rely on fixed parameters, limiting adaptability and accelerating energy storage depletion. To address these limitations, a coordinated control strategy based on dynamic weighting coefficients and model predictive control (MPC) is proposed. First, a dynamic weighting mechanism is designed to adaptively adjust the contributions of virtual inertia and droop control based on the system frequency state and the energy storage system’s (ESS) state of charge (SOC), thereby avoiding abrupt power variations and maintaining the SOC within safe limits. Second, an MPC-based rolling optimization model is established to continuously allocate the active power outputs between the doubly fed induction generator (DFIG) and the ESS, aiming to minimize both frequency deviations and regulation costs. Simulation results demonstrate the superiority of the proposed strategy. Under a step load disturbance, the maximum frequency deviation is reduced by 11.3%, and the peak time is shortened by 13% compared to conventional droop control. Furthermore, under continuous load fluctuations, the proposed approach significantly mitigates SOC depletion and minimizes system frequency fluctuations, proving its effectiveness in enhancing the frequency resilience of wind-storage combined systems. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

Reclaiming saline–alkaline soil is critical for food security and land expansion. While paddy rice is the key pioneer crop for remediation, the soil fertility–yield relationship remains poorly understood. To optimize remediation strategies, this study evaluated soil fertility under 16 agronomic treatments—integrating irrigation quality, fertilizer regimes, and soil amendments—across three rice growth stages (tillering, heading, and maturity) in the Yellow River Delta using the minimum data set (MDS), integrated soil fertility index (SFI), and random forest models. Saline water irrigation increased soil salinity by 24.6%, while straw returning and desulfurization gypsum reduced salinity by 18.3% and 22.7%, respectively. Straw, biochar, and desulfurization gypsum significantly influenced soil organic carbon (SOC), total nitrogen (TN), inorganic nitrogen (NH₄⁺-N, NO₃⁻-N), and available phosphorus (AP), with effects varying across growth stages. Growth-stage-specific MDS indicators were significantly correlated with SFI based on the total data set (R² = 0.70, 0.65, and 0.81, p < 0.01), and stage-specific SFI was significantly positively related to rice yield. Notably, heading-stage SFI, although relatively low, explained the highest yield variance (R² = 0.51, p < 0.01) and prediction accuracy (%IncMSE = 25.22), especially under conventional NPK combined with full straw incorporation and desulfurization gypsum. These findings highlight the critical role of heading-stage soil fertility in regulating rice production, providing a targeted nutrient management blueprint for saline–alkaline paddy fields in the Yellow River Delta. Overall, this study offers a reliable scientific template to enhance yield and promote sustainable agriculture in comparable saline–alkaline paddy fields globally. Full article

This study explores a photovoltaic/thermal (PV/T)-based electrolysis system designed for dual production of hydrogen fuel and domestic hot water (DHW), providing a sustainable energy solution amid rising global emissions. A dynamic rule-based control mechanism with hysteresis thresholds on hydrogen-storage state of charge (SoC) is implemented to balance electrolyzer operation with intermittent solar availability, maintaining PV/T power outputs while preventing storage overfilling and minimizing start–stop cycling. The system is assessed across 27 geographically diverse cities spanning a wide range of solar irradiation and energy price structures. Annual hydrogen yields range from 20 kg/yr in high-latitude locations (Helsinki, Stockholm) to 33.5 kg/yr in high-irradiation regions (Riyadh, Abu Dhabi), while the levelized cost of hydrogen (LCOH) spans from 6.47 USD/kg (Riyadh) to 22.86 USD/kg (Helsinki). Economically, the system achieves its strongest performance in solar-rich, high-energy-cost environments: Rome records the highest net annual cash flow (858.9 USD/yr) and shortest payback period (2.47 years), followed by Davos, Madrid, Brasília, and Canberra. In contrast, locations with subsidized energy tariffs—such as Algiers, Kyiv, and Tehran—yield low or negative net cash flows, rendering the system economically unviable without policy support. Environmental analysis reveals annual CO₂ avoidance ranging from 0.33 ton/yr (Stockholm) to 2.97 ton/yr (Riyadh), with a global mean of 1.095 ton/yr and a combined total of approximately 29.6 tons/yr across all examined sites. A machine learning model is developed to generalize performance predictions across unseen locations, achieving leave-one-out (LOO) R² values of 0.953 (net cash flow), 0.935 (LCOH), and 0.947 (LCO-DHW), with mean absolute errors below ±1 USD/kg and ±0.03 USD/kWh. The findings confirm that, under fixed capital cost assumptions, local electricity price and solar irradiation are the dominant drivers of economic viability, while grid carbon intensity and solar resource jointly govern environmental performance, with markets offering irradiation above 1500 kWh/m²·yr and electricity prices exceeding 0.2 USD/kWh representing the most promising deployment targets. Full article

The smart hybrid microgrid energy management system is based on photovoltaic (PV) arrays, wind turbines, battery energy storage, and diesel generators, supplying clean, stable, and cost-effective energy to rural villages. Predictive control, load-demand/load-following, and SOC optimization enable supply and demand adjustments for stable operation and reduced emissions/diesel consumption. MATLAB 2024a simulations support the concept that this system operates more sustainably, reliably, and efficiently when a compromise is made between conventional and renewable sources. By addressing the reliability issue of renewable energy’s intermittent production, such hybrid systems can provide the consistent power necessary for economic productivity and health/education in rural villages. Full article

Accurate state-of-charge (SOC) prediction is critical for estimating driving range and ensuring the reliability of electric vehicle (EV) battery management systems. Although machine learning-based SOC prediction models achieve high accuracy, their complex nonlinear structures limit interpretability and hinder practical deployment. This study proposes an automated interpretation framework that integrates a multimodal large language model (MLLM) with Shapley interaction quantification (SHAP-IQ) to explain SOC prediction results. An XGBoost-based SOC prediction model is developed, and SHAP-IQ is employed to analyze both main effects of individual input variables (order 1) and pairwise feature interactions (order 2). SHAP-IQ visualizations and attribution values are provided as inputs to MLLM, which generates instance-level natural language explanations, while cross-validation and aggregation procedures ensure consistency. Experiments using real-world driving data collected from a BMW i3 show that XGBoost outperforms benchmark models in SOC prediction accuracy. The results indicate that, for the analyzed instances, SOC predictions are primarily governed by electrical variables such as battery voltage and current, whereas driving and environmental variables mainly affect the prediction through interaction effects. The proposed framework demonstrates the potential to improve the interpretability of SOC prediction models and can be extended to other energy systems in EVs employing complex machine learning models. Full article

Soil Organic Matter (SOM) and Soil Organic Carbon (SOC) are essential for regulating ecosystem functions, soil fertility, and influencing climate change processes, especially in semi-arid regions. The recent improvements in remote sensing instruments and the development of artificial intelligence methodologies, such as machine learning, enable an improved understanding of carbon dynamics, facilitate the estimation of SOC content, and support predictive modeling. This study presents an integrated framework to analyze past and future carbon dynamics in the Sfax Governorate (Tunisia). Land-use and land-cover (LULC) maps for the years 2019, 2020, 2022, and 2024 were generated using a Random Forest algorithm applied to multispectral satellite data in the Google Earth Engine platform, achieving high classification accuracy (overall accuracy up to 0.90). Carbon stocks and their temporal variations were estimated using the InVEST Carbon Storage and Sequestration model, while carbon emissions and the Net Ecosystem Carbon Balance (NECB) were derived by integrating land-use-specific emission factors. Future LULC scenarios for 2030 were simulated through a Cellular Automata model under three alternative development pathways: conservation-oriented (CONS), business-as-usual (BAU), and urban expansion (URB+). The study demonstrates how the integration of machine learning, remote sensing, and ecosystem modeling supports spatially explicit assessment of SOC-related carbon dynamics and provides useful insights for land management and climate mitigation strategies. Full article

The analysis of soil carbon helps various sectors, including agriculture, in the context of monitoring soil health. In precision agriculture, decisions are made on the basis of site-specific information and thus have the potential to increase crop productivity more than is possible with traditional high-input agriculture. Site-specific information-based nutrition management, pest and disease management, and water management are the main areas of interest in the era of precision agriculture. Soil organic carbon (SOC) is one of the main components of the carbon cycle and impacts soil physical and chemical properties. Soil color is considered an indicator of soil carbon. In relation to soil physical properties, soil color has been used to determine SOC level and classification throughout history in a qualitative manner, and recently, researchers have shown interest in relating soil color data to quantify soil chemical properties. From spectroscopy-based color analysis to image-based color analysis, research has shown strong relationships between SOC and color properties. Therefore, with the improvement of technology to create smaller and portable sensors, the potential exists to automate the processes of soil chemical analysis to use them in precision agriculture. Two of the major limitations of these methodologies in research are the number of known soil samples required to calibrate a model (the majority of the models require more than 100 samples) and the use of expensive spectrometers with complex processes. Thus, the potential of individual farmers to deploy these methods is limited. This research was conducted to develop a methodology with complete guidelines and a set of tools to allow farmers to analyze SOC themselves. Furthermore, by encouraging farmers to analyze their farmland soils for SOC and update the data, the research enables them to potentially use this information to manage their agronomic practices, including the addition of organic fertilizer to reduce soil carbon pool inefficiencies and decisions regarding the mode of tillage and water management. During this research, three sensors and different combinations of sensors were used to capture soil surface color, temperature, and reflectance and were considered for model development. The highest-model-fit equation was obtained from the thermal image and red, green, and blue (RGB) image combinations (R² = 0.65 and MSE = 0.0335). The variables used for X from the color models were hue values and redness (a), and those from the thermal image minimum and maximum temperature data were used. Finally, using a regression equation along with the image data and SOC data from the chemical analysis, a farmer-feedback-based SOC prediction model was developed. Full article

State of charge (SOC) estimation is a key function in battery management systems (BMSs) because it directly affects safe operation and available energy prediction. In embedded BMS platforms, information from multiple cells must be processed within tight computation and memory budgets. The estimator therefore needs to balance accuracy and implementation cost. This paper presents a lightweight SOC estimation method based on the relationship between open circuit voltage and state of charge (OCV–SOC) in lithium-ion batteries, together with a standalone gauge IP based on finite-state machine (FSM) control. The reference OCV–SOC curve of a commercial 3.7 V lithium-ion cell is approximated by a two-region quadratic model. The IP estimates OCV from the measured terminal voltage with equivalent series resistance (ESR) correction and updates SOC iteratively. To obtain predictable runtime behavior and to suppress oscillatory behavior near convergence, the hardware combines a 1-LSB termination rule with a guard based on a maximum iteration count of $N_{max}=10$. Real-time validation on an FPGA-based battery measurement testbed achieves an overall normalized mean absolute error (NMAE) of 1.6% over charge and discharge data. When synthesized for an Artix-7 XC7A100T, the proposed gauge IP used only 504 LUTs (0.79%) and 580 FFs (0.46%). A TSMC 28 nm MPW implementation further demonstrates feasibility for integration at chip level. Full article