Search Results (23,654)

The growing need for efficient and safe high-energy lithium-ion batteries (LIBs) in electric vehicles and grid storage necessitates advanced internal monitoring solutions. This work presents a comprehensive simulation model of a novel integrated optical sensor based on ethylene carbonate-filled photonic crystal fiber (EC-PCF). The proposed design synergistically combines a chirped fiber Bragg grating (FBG) and an extrinsic Fabry–Pérot interferometer (EFPI) on a multiplexed platform for the multifunctional sensing of refractive index (RI), temperature, strain, and pressure (via strain coupling) within LIBs. By matching the RI of the PCF cladding to the battery electrolyte using ethylene carbonate, the design maximizes light–matter interaction for exceptional RI sensitivity, while the cascaded EFPI enhances mechanical deformation detection beyond conventional FBG arrays. The simulation framework employs the Transfer Matrix Method with Gaussian apodization to model FBG reflectivity and the Airy formula for high-fidelity EFPI spectra, incorporating critical effects like stress-induced birefringence, Transverse Electric (TE)/Transverse Magnetic (TM) polarization modes, and wavelength dispersion across the 1540–1560 nm range. Robustness against fabrication variations and environmental noise is rigorously quantified through Monte Carlo simulations with Sobol sequences, predicting temperature sensitivities of ∼12 pm/°C, strain sensitivities of ∼1.10 pm/$\mathsf{\mu }$$\mathsf{\epsilon }$, and a remarkable RI sensitivity of ∼1200 nm/RIU. Validated against independent experimental data from instrumented battery cells, this model establishes a robust computational foundation for real-time battery monitoring and provides a critical design blueprint for future experimental realization and integration into advanced battery management systems. Full article

The evolution of the generation mix towards deep decarbonization poses pressing questions about the role of hydropower and its possible share in the future mix. Most technical–economic analyses of deeply decarbonized systems either rule out hydropower growth due to lack of additional hydro resources or take it into account in terms of additional reservoir capacity. This paper analyzes a generation mix made of photovoltaic, wind, open-cycle gas turbines, electrochemical storage and hydroelectricity, focusing on the optimal generation mix’s reaction to different methane gas prices, hydroelectricity availabilities, pumped hydro reservoir capacities, and mean filling durations for hydro reservoirs. The key feature of the developed model is the sizing of both optimal peak power and reservoir energy content for hydropower. The results of the study point out two main insights. The first one, rather widely accepted, is that cost-effective decarbonization requires the greatest possible amount of hydro reservoirs. The second one is that, even in the case of totally exploited reservoirs, there is a strong case for increasing hydro peak power. Application of the model to the Italian generation mix (with 9500 MWp and 7250 MWp of non-pumped and pumped hydro fleets, respectively) suggests that it is possible to achieve methane shares of less than 10% if the operating costs of open-cycle gas turbines exceed 160 EUR/MWh and with non-pumped and pumped hydro fleets of at least 9200 MWp and 28,400 MWp, respectively. Full article

This study aims to reduce reverse power and improve frequency regulation performance in hydropower systems. To achieve this objective, a refined hydropower plant (HPP) simulation model is developed and coupled with a battery energy storage system (BESS), implementing an Integrated Adaptive Virtual Droop Control (IAVDC) strategy. The refined HPP model achieves a simulation accuracy of 98.5%, representing a 26.2% improvement over conventional simplified models. With the BESS integrated under the IAVDC strategy, reverse power is completely eliminated, and frequency regulation time is substantially shortened. The results demonstrate that the joint HPP-BESS frequency regulation effectively mitigates the adverse impact of water hammer, while the proposed IAVDC strategy enhances system responsiveness and reduces frequency recovery time, thereby improving the quality of primary frequency control. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

Lithium-ion batteries are widely used in energy storage and power systems due to their high energy density, long cycle life, and stability. Accurate prediction of the state of health (SOH) of batteries is critical to ensuring their safe and reliable operation. However, the prediction task remains challenging due to various complex factors. This paper proposes a hybrid TCN–Transformer–BiLSTM prediction model for battery SOH estimation. The model is first validated using the NASA public dataset, followed by further verification with dynamic operating condition simulation experimental data. Health features correlated with SOH are identified through Pearson analysis, and comparisons are conducted with existing LSTM, GRU, and BiLSTM methods. Experimental results demonstrate that the proposed model achieves outstanding performance across multiple datasets, with root mean square error (RMSE) values consistently below 2% and even below 1% in specific cases. Furthermore, the model maintains high prediction accuracy even when trained with only 50% of the data. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Copper is one of the most used metals today due to its wide range of applications. Traditionally, this metal has been primarily extracted through pyrometallurgical methods, which presents several environmental and energy-related drawbacks. An alternative is hydrometallurgy, which has achieved acceptable copper extraction rates. However, this process has not found widespread industrial application due to operational challenges and the complexity associated with the selective recovery of copper ions from the Pregnant Leach Solution (PLS), especially due to the coexistence of copper and iron ions, complicating the efficient separation of both metals. In this work, the use of aluminum shavings as a cementation agent is proposed, analyzing variables such as the initial shaving concentration (2.5, 5, 10, 15, and 20 g/L), the agitation speed (0, 200, and 400 rpm), and a temperature of 20, 30, and 40 °C. The results demonstrated selective copper cementation, achieving a 100% recovery in 30 min under stirring conditions of 400 rpm. The analysis performed using X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) revealed the formation of solid phases such as metallic copper (Cu), aluminum hydroxide [Al(OH)₃], and elemental sulfur (S). Additionally, it was observed that the iron ion concentration remained constant throughout the experiment, indicating a high selectivity in the process. The kinetic analysis revealed that the reaction follows a first-order model without stirring. An activation energy of 62.6 kJ/mol was determined within the experimental temperature range of 20–40 °C, confirming that the process fits the chemical reaction model. These findings provide a deeper understanding of the system’s behavior, highlighting its feasibility and potential for industrial-scale applications. Full article

A novel bio-gelatin fiber-reinforced composite (BFRC) was first developed by incorporating industrial bone glue/gelatin as the matrix, magnesium oxide (MgO) as an additive, and natural or synthetic fibers as reinforcement. Systematic tests evaluated mechanical, impact, and thermal performance, alongside microstructural mechanisms. Results showed that polyethylene (PE) fiber-reinforced composites achieved a tensile strength of 3.40 MPa and tensile strain of 10.77%, with notable improvements in compressive and flexural strength. PE-based composites also showed excellent impact energy absorption, while bamboo fiber-reinforced composites exhibited higher thermal conductivity. Microstructural analysis revealed that coordination between Mg²⁺ ions and amino acids in gelatin formed a stable cross-linked network, densifying the matrix and improving structural integrity. A multi-criteria evaluation using the TOPSIS model identified the BC-PE formulation as the most balanced system, combining strength, toughness, and thermal regulation. These findings demonstrate that ionic coordination and fiber reinforcement can overcome inherent weaknesses of gelatin matrices, offering a sustainable pathway for building insulation and cushioning packaging applications. Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

Power outages pose considerable risks to the reliability of electric grids, affecting both consumers and utilities through service disruptions and potential economic losses. This study analyzes a historical outage dataset from a Regional Transmission Organization (RTO) to reveal key patterns and trends that suggest outage management strategies. By integrating exploratory data analysis, predictive modeling, and a Large Language Model (LLM)-based interface integration, as well as data visualization techniques, we identify and present critical drivers of outage duration and frequency. A random forest regressor trained on features including planned duration, facility name, outage owner, priority, season, and equipment type proved highly effective for predicting outage duration with high accuracy. This predictive framework underscores the practical value of incorporating planning information and seasonal context in anticipating outage timelines. The findings of this study not only deepen the understanding of temporal and spatial outage dynamics but also provide valuable insights for utility companies and researchers. Utility companies can use these results to better predict outage durations, allocate resources more effectively, and improve service restoration time. Researchers can leverage this analysis to enhance future models and methodologies for studying outage patterns, ensuring that artificial intelligence (AI)-driven methods can contribute to improving management strategies. The broader impact of this study is to ensure that the insights gained can be applied to strengthen the reliability and resilience of power grids or energy systems in general. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as ‘Watch_TV’, ‘Sleep’, ‘Work_On_Computer’, and ‘Cook_Dinner’, is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models—XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)—are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation. Full article

To investigate diquark correlation in baryons, the baryon spectra with different light–heavy quark combinations are calculated using Gaussian expansion method within both the naive quark model and the chiral quark model. By computing the diquark energies and separations between any two quarks in baryons, we analyze the diquark effect in the $ud$-q/Q, $us$-Q, $ss$-q/Q, and $QQ$-q/Q systems (where $q=u,d$, or s; $Q=c,b$). The results show that diquark correlations exist in baryons. In particular, for $qq$-Q and $QQ$-q systems, the same type of diquark exhibits nearly identical energy and size across different baryons. In the orbital ground states of baryons, scalar–isoscalar diquarks have lower energy and a smaller size compared to vector–isovector diquark, which qualifies them as “good diquarks”. In $QQ$-q systems, a larger mass of Q leads to a smaller diquark separation and a more pronounced diquark effect. In $qq$-Q systems, the separation between the two light quarks remains larger than that between a light and a heavy quark, indicating that the internal structure of such diquarks must be taken into account. A comparison between the naive quark model and the chiral quark model reveals that the introduction of meson exchange slightly increases the diquark size in most systems. Full article

Using systems with machine learning technologies for process automation is a global trend in agriculture. However, implementing this technology comes with challenges, such as the need for a large amount of computing resources under conditions of limited energy consumption and the high cost of hardware for intelligent systems. This article presents the possibility of applying a modern ESP32 microcontroller platform in the agro-industrial sector to create intelligent devices based on the Internet of Things. CNN models are implemented based on the TensorFlow architecture in hardware and software solutions based on the ESP32 microcontroller from Espressif company to classify objects in crop fields. The purpose of this work is to create a hardware–software complex for local energy-efficient classification of images with support for IoT protocols. The results of this research allow for the automatic classification of field surfaces with the presence of “high attention” and optimal growth zones. This article shows that classification accuracy exceeding 87% can be achieved in small, energy-efficient systems, even for low-resolution images, depending on the CNN architecture and its quantization algorithm. The application of such technologies and methods of their optimization for energy-efficient devices, such as ESP32, will allow us to create an Intelligent Internet of Things network. Full article

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article