Search Results (1,921)

An algorithm has been implemented and it is provided in this article as an executable program to extract the five solar cell parameters within the one-diode solar cell model. Boole’s integration rule has been put into practice to integrate the current minus the short-circuit current, yielding a more accurate Co-Content function. Afterwards, the Co-Content function is fitted to a second-degree polynomial in two variables, namely, the voltage and the current minus the short-circuit current, providing six fitting constants. The five solar cells are deduced from these six fitting constants. This algorithm has been implemented in an automatic program that performs the calculations. The program also obtains the standard deviations of the fitting errors, which are used to obtain the standard deviations of the five solar cell parameters. The program reports to the user the results in three text files, from which the user can easily copy-paste the results into softwares like Origin, Word, or Excel. A program to smooth the current voltage curves is also provided. Two videos are also available, one explaining how to profit from this executable program, and the other one how to use the smoothing program. Full article

Electric-field-sensitive dielectrics play a crucial role in electric field induction sensing and related capacitive conversion, with interfacial polarization and charge accumulation largely determining the signal output. This paper introduces graphene/transition metal dichalcogenide (TMD) (MoSe₂, MoS₂, and WS₂) heterojunctions as functional fillers to enhance the dielectric response and electric-field-induced voltage output of flexible polydimethylsiloxane (PDMS) composites. Density functional theory (DFT) calculations were used to evaluate the stability of the heterojunctions and interfacial electronic modulation, including binding behavior, charge redistribution, and Fermi level-referenced band structure/total density of states (TDOS) characteristics. The calculations show that the graphene/TMD interface is primarily controlled by van der Waals forces, exhibiting negative binding energy and significant interfacial charge rearrangement. Based on these theoretical results, graphene/TMD heterojunction powders were synthesized and incorporated into polydimethylsiloxane (PDMS). Structural characterization confirmed the presence of face-to-face interfacial contacts and consistent elemental co-localization within the heterojunction filler. Dielectric spectroscopy analysis revealed an overall improvement in the dielectric constant of the composite materials while maintaining a stable loss trend within the studied frequency range. More importantly, calibrated electric field induction tests (based on pure PDMS) showed a significant enhancement in the voltage response of all heterojunction composite materials, with the WS₂-G/PDMS system exhibiting the best performance, exhibiting an electric-field-induced voltage amplitude 7.607% higher than that of pure PDMS. This work establishes a microscopic-to-macroscopic correlation between interfacial electronic modulation and electric-field-sensitive dielectric properties, providing a feasible interface engineering strategy for high-performance flexible dielectric sensing materials. Full article

This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

This study presents a modular battery charging system based on a DC–DC buck converter with proportional–integral (PI) control, developed to support hands-on learning in power electronics education. In response to the need for flexible experimental platforms, the system is designed to bridge theoretical concepts of power conversion and control with practical implementation. The proposed setup employs cascaded current and voltage control loops to achieve constant current (CC) and constant voltage (CV) charging modes, while its modular hardware architecture allows modification of key parameters such as inductance, capacitance, and circuit topology. The control algorithms are implemented on a microcontroller, and real-time data acquisition is integrated using the ThingSpeak platform for monitoring system behaviour. Experimental results show that the current control loop recovers to its reference value within approximately 6 ms under abrupt load variations, whereas the voltage control loop settles within approximately 15 ms, demonstrating stable closed-loop performance. In addition, the system successfully charges a 12 V lead-acid battery following a standard CC–CV charging profile. Overall, the proposed experiment kit provides an effective educational platform and a practical basis for further exploration of battery charging strategies and power converter control. Full article

In this study, the influence of the electrochemical oxidation process on Ti-Grad 23 alloy (Ti6Al4V ELI) in 1 M H₃PO₄, under applied voltages between 200 and 275 V, at a constant time of 1 min, is analyzed. The structural, morphological, and wettability properties of the TiO₂ anodic layers obtained were investigated by X-ray diffraction (XRD), energy dispersive electron microscopy (SEM-EDS), contact angle measurements, and chemical corrosion. XRD analysis showed the development and intensification of anatase and brookite phases, with increased crystallite size after electrochemical oxidation. SEM/EDS characterization confirmed the formation of an inhomogeneous porous TiO₂ layer, with pore diameters ranging from 98 to 139 nm and a significant increase in oxygen content. Contact angle measurements demonstrate enhanced hydrophilicity for all oxidized samples, with progressively lower values as the applied voltage increased. Chemical corrosion tests in Ringer solution and Ringer + 40 g/L H₂O₂ indicated that oxidized surfaces maintain structural stability in physiological media, whereas exposure to oxidizing environments induces partial pore closure and crack formation due to localized corrosion. The optimal anodizing condition was identified at 200 V for 1 min, yielding a uniform distribution of pores and improved morpho-functional characteristics suitable for biomedical applications. The optimal electrochemical oxidation conditions were identified at 200 V for 1 min, ensuring a uniform pore distribution. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for the safe operation of electric vehicles and energy storage systems. However, most existing methods rely on complete charging curves or manual feature engineering, making them difficult to adapt to practical scenarios where only limited charging segments are available. To fully exploit degradation information from limited charging data, this paper proposes a dual-modal mixture of Kolmogorov–Arnold network (DM-MoKAN) for lithium-ion battery SOH estimation using only early-stage constant-current charging voltage data. The proposed method incorporates three synergistic modules: an image branch, a sequence branch, and a dual-modal fusion regression module. The image branch converts one-dimensional voltage sequences into two-dimensional Gramian Angular Difference Field (GADF) images and extracts spatial degradation features through a lightweight network integrating Ghost convolution and efficient channel attention (ECA). The sequence branch employs a patch-based Transformer encoder to directly model local patterns and long-range dependencies in the raw voltage sequence. The dual-modal fusion module concatenates features from both branches and feeds them into a MoKAN regression head composed of multiple KAN experts and a gating network for adaptive nonlinear mapping to SOH. Experimental results demonstrate that DM-MoKAN outperforms various baseline methods on both Oxford and NASA datasets, achieving average RMSE/MAE of 0.28%/0.19% and 0.89%/0.71%, respectively. Ablation experiments further verify the effective contributions of the dual-modal fusion strategy, ECA attention mechanism, and MoKAN regression head to estimation performance improvement. Full article

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article

This paper formulates a control framework grounded in prescribed performance control (PPC) and combined with a dynamic error modulation function. The proposed framework addresses the control challenges of DC–DC boost converters under sudden power variations caused by constant power loads (CPLs). A sine kernel-based prescribed performance function with smoothly decaying characteristics is designed to form a dynamic performance boundary that gradually tightens as the system state evolves. Furthermore, to effectively eliminate the restriction of traditional PPC on the system’s initial state, a time-varying modulation function is introduced. This function dynamically scales the tracking error, thereby improving the system’s adaptability at the initial state. A neural network disturbance observer (NNDO) is employed to approximate and compensate for unknown nonlinearities and external disturbances, thereby enhancing system robustness and adaptability. Consequently, a prescribed performance controller that integrates dynamic error modulation and a dual-channel NNDO is proposed. The proposed controller not only guarantees that the tracking error satisfies the prescribed performance constraints but also avoids the computation of high-order derivatives. Simulation results demonstrate that the proposed method maintains bounded convergence of the tracking error and achieves smooth voltage regulation during CPL variations. The results further exhibit excellent dynamic response and steady-state performance. Full article

Hybrid trolleybuses (HTBs) with in-motion charging (IMC) can extend zero-emission service using existing catenary, but high on-wire charging powers may concentrate loads and accelerate battery aging. We present a data-driven simulation that replays recorded high-resolution Controller Area Network (CAN) logs through a per-vehicle electrical model with (Constant-Current/Constant-Voltage) (CC/CV) charging and a stress-map aging estimator, a configurable partial catenary overlay, and fleet aggregation by simple summation and an iterative node-voltage analysis of a resistor-network catenary model. A parameter sweep across battery sizes, upper state of charge (SoC) bounds, and charging power caps compares a minimal “charge-whenever-possible” policy with a per-vehicle lookahead (“oracle”) policy that spreads charging over available catenary time. Results show that lowering maximum charging power and/or the upper SoC bound reduces capacity fade, while energy-demand differences are small. Fleet load profiles are dominated by timetable-driven concurrency using 40 recorded days overlaid into one synthetic day: varying per-vehicle power or target SoC has little effect on peak demand; per-vehicle lookahead does not flatten the peak. The node-voltage analysis indicates catenary efficiency around 97% and fewer undervoltage events at lower charging powers. We conclude that per-vehicle policies can reduce battery stress, whereas peak shaving requires cooperative, fleet-level scheduling. Full article

The properties of carbon-based materials with nanometric size support their use in numerous applications, such as optoelectronics and energy devices, bioimaging, photodetectors, and sensors. Among the various nanostructure fabrication methods, pulsed laser ablation in liquids (PLA) is widely recognized for its simplicity and rapid processing. It is considered an environmentally friendly synthesis, as it enables nanostructure fabrication in pure liquids without chemical reagents, activators, or vacuum systems, in line with the increasing interest in sustainable and green nanotechnologies. A great challenge of PLA is the reproducibility of the size and shape of the produced structure. This can be accomplished by selection of the proper laser parameters and characteristics of the used liquid. This study is focused on the comparison of the synthesis of graphene-based nanostructures by electric-field-assisted pulsed laser ablation of a graphite target immersed in distilled water and deionized water, used as separate liquid media, without the use of chemical reagents. This is an innovative and environmentally friendly approach for the production of graphene nanoparticles. The laser parameters were kept constant throughout the experiments, while different voltage values were applied between the electrodes immersed in the liquid medium. The applied electric field significantly influences plasma dynamics, cavitation bubble evolution, and post-ablation nanoparticle growth processes, enabling controlled tuning of nanoparticle size and morphology. The optical properties of the obtained suspensions were evaluated by UV–Vis and FTIR spectroscopies. Atomic force microscopy revealed the composition, morphology, and quality of the formed structures. Full article

The progressive replacement of synchronous machines by inverter-based resources (IBRs) reduces system inertia and short-circuit strength, making power systems more vulnerable to frequency and voltage instabilities. Grid-forming (GFM) inverters can mitigate these issues by establishing voltage and frequency references, emulating inertia and enabling autonomous operation during islanding, while grid-following (GFL) inverters mainly contribute to reactive power support. This paper evaluates the capability of GFM inverters to provide grid support under both grid-connected and islanded conditions at the distribution level. Electromagnetic transient (EMT) simulations in MATLAB/Simulink R2022b were performed on a 20 kV radial microgrid comprising GFM and GFL inverters and aggregated load. Small disturbances, including phase-angle jumps and voltage steps at the point of common coupling, were introduced while varying the GFM share and virtual inertia constants. Also, local variables were assessed during islanded operation and separation process. Results indicate that maintaining a GFM share above approximately 30–40% with inertia constants exceeding 2 s significantly enhances frequency stability, supports successful transitions to islanded operation, and improves overall resilience. The study highlights the complementary roles of GFM and GFL in enabling the stable and resilient operation of converter-dominated distribution systems. Full article

The surface morphology of lithium metal electrodes evolves markedly during cycling, modulating interfacial kinetics and increasing the risk of dendrite-driven internal short circuits. Here, we infer this morphological evolution from direct-current (DC) signals by analyzing open-circuit voltage (OCV) transients after constant current interruptions. The OCV exhibits a rapid initial decay followed by a transition to a slower long-time decay. With repeated plating, this transition shifts to earlier times, thereby increasing the contribution of long-term relaxation. We quantitatively analyze this behavior using an equivalent circuit with a transmission-line model (TLM) representing the electrolyte-accessible interfacial region of the electrode, discretized into ten depth-direction segments. Tracking segment-wise changes in resistances and capacitances with cycling enables morphology estimation. Repeated plating strongly increases the double-layer area near the current collector, while the charge-transfer-active surface shifts toward the separator side, showing progressively smaller and eventually negative changes toward the current-collector side. Together with the segment-resolved time constants, these trends indicate that lithium deposition becomes increasingly localized near the separator-facing surface, while the interior becomes more tortuous, consistent with post-mortem observations. Overall, the results demonstrate that DC voltage-relaxation analysis combined with a TLM framework provides a practical route to track lithium metal electrode surface evolution in Li-metal-based cells. Full article

The global phase-out of sulfur hexafluoride (SF6), an insulating gas with high global warming potential (GWP), has driven the search for eco-friendly alternatives in high-voltage equipment. Perfluoroisobutyronitrile (C4F7N) emerges as a promising candidate due to its low GWP and high dielectric strength. However, its chemical stability under circuit breaker conditions, especially when interacting with vaporized contact materials such as silver, remains a key concern. This study investigates the decomposition mechanisms of C4F7N in the presence of silver vapor using quantum chemical calculations at the B3LYP/LanL2DZ level. A reaction network comprising 35 pathways and 12 transition states were identified. All structures were confirmed as valid stationary points via frequency analysis and intrinsic reaction coordinate (IRC) calculations. Three primary reaction pathways between C4F7N and Ag were delineated, leading to secondary reactions that generate low-weight molecules and Ag-containing species such as AgF and AgCN. Key energy barriers and temperature-dependent equilibrium constants (K_eq) were determined to evaluate pathway feasibility. This work provides fundamental insights into the high-temperature interfacial chemistry of C4F7N with Ag, offering essential data for assessing its material compatibility and long-term reliability as a sustainable insulation medium in power systems. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article