Search Results (14,451)

Active-power ceilings create a control challenge in droop-controlled grid-forming converter networks because the electrical response is faster than the measurements and outer control. Projected and projection-free power limiting use filtered active power in the outer power–frequency channel and therefore cannot act directly on the first electrical power peak. This paper proposes constraint-activated projection-free control, which coordinates a shaped projection-free multiplier with a bounded resistance term in the capacitor-voltage reference driven by instantaneous terminal power. A general full-order dynamic model describes the converters, controllers, and network without tying the formulation to a particular benchmark. Local well-posedness is established, and the proposed controller is shown to preserve the constrained projection-free equilibrium and active-branch Jacobian, allowing the same full-order stability assessment. Across ten tested scenarios with unchanged controller parameters, the proposed controller reduces peak power exceedance by 38.7–55.4% and accumulated excess energy by 34.1–62.2%. The corresponding DC-buffer requirement decreases without activating the independent current limiter, which isolates the source-side power constraint from AC overcurrent. A network-level study demonstrates sequential transitions between one and two constrained sources while the remaining converter supplies the feasible power imbalance. Full-order stability verification, component studies, and parameter sweeps establish the role and useful range of each controller path. Full article

Electrochemical parameter identification is crucial for evaluating the electrochemical processes in proton exchange membrane water electrolysis (PEMWE). Conventional characterization techniques-including polarization-curve fitting, electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and current interruption (CI)-face significant limitations for rapid diagnostics under high-current dynamic operation, arising from constraints in instrument current rating, measurement time, zero-current control, and noise amplification in numerical differentiation. In this study, we present a simple current step (CS) method to accurately identify key electrochemical parameters and perform overpotential breakdown by using a simplified equivalent circuit model with a current source. To address the numerical instability in derivative calculation caused by sampling noise during voltage transient analysis, a physics-informed neural network (PINN) is introduced to enhance signal smoothness while guaranteeing physical consist ency. Compared with standard characterization, the proposed CS-PINN method demonstrates high accuracy, with an error of less than 2% in overpotential breakdown, less than 5.3% in ohmic resistance, and 2.8% in the Tafel slope (at 5 A/cm²). These results confirm that the CS-PINN method provides a fast, accurate, and equipment-friendly route for rapid electrochemical parameter identification in PEMWE. Full article

This article presents a review and comparative analysis of converter architectures for electrolyzer systems, covering alternating current (AC) -grid-connected, direct-current (DC) -grid-connected, and renewable-energy-connected systems, as well as isolated and non-isolated configurations. The study classifies and compares key converter topologies based on engineering criteria, including voltage gain, efficiency, device count, control complexity, and implementation feasibility. Furthermore, the relationships among converter structures, power-source characteristics, and electrolyzer-system requirements are analyzed to reveal system-level engineering trade-offs. The analysis demonstrates that converter suitability depends on the combined requirements of the power source, galvanic isolation, electrolyzer characteristics, operating conditions, and application-specific engineering priorities. In addition, wide-bandgap semiconductor devices and electrolyzer operating characteristics are discussed as important factors in converter selection, particularly for improving converter efficiency, reducing current ripple, increasing power density, and supporting dynamic operation. This article therefore provides a systematic framework for converter classification and selection according to power-source characteristics, electrolyzer requirements, and application power levels. Full article

Accurate parameterization of the photovoltaic single-diode model is usually obtained from complete current–voltage (I-V) measurements. However, full I-V curve tracing is not always available in real monitoring environments, where the most accessible variables are irradiance and module temperature. This paper proposes a hybrid methodology for reconstructing the five parameters of the single-diode model from irradiance and temperature data. The method first estimates the maximum-power point and the remaining remarkable points of the I-V curve as well as the photocurrent ($I_{ph}$) through regression models calibrated on measured data. These predicted points are sufficient to solve the SDM equation. A numerical approach is then used to identify the five SDM parameters while enforcing physical admissibility constraints. The method is validated using NREL outdoor datasets from three locations and several photovoltaic technologies. The results show that the maximum-power current is estimated with very high reliability, with $R^2$ values close to unity in almost all cases. Voltage estimation is less stable and depends more strongly on technology and temperature sensor location. The reconstructed I-V curves are physically admissible for most crystalline silicon, HIT, and CdTe modules, whereas CIGS and amorphous silicon modules exhibit lower admissibility. The proposed method should therefore be understood as an environmental-data-driven reconstruction tool when complete I-V curves are unavailable, rather than as a replacement for direct full-curve fitting techniques such as TSLLS or Reduced Form. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

Reliable pack-level battery systems capable of safely handling instantaneous high-C-rate discharge above 10C during take-off, climb, and hovering are required for the commercialization of urban air mobility (UAM) aircraft. However, pack-level studies on wide-range C-rate characteristics of battery systems for UAM applications remain very limited, and most previous studies have been restricted to single-cell experiments or battery-pack simulations. In this study, a 24S1P test battery pack using nickel–cobalt–manganese (NCM) pouch cells, with a nominal voltage of 88.8 V and a capacity of 22 Ah, was designed and fabricated. A two-level battery management system (BMS) based on the LTC6803G-4 was also developed. To evaluate the charge–discharge characteristics of the battery system, constant-current discharge tests were conducted under five conditions ranging from 0.2C (4.4 A) to 10.68C (235 A), and charging tests were performed over the range of 0.2C–2C. The discharge test results showed that the capacity retention remained within 97.5–100.0% in the 1C–5C range, confirming excellent power capability. Continuous discharge operation was confirmed at 10.68C, the maximum discharge condition considered for vertical take-off and climb. Under this condition, the capacity decreased to 16.26 Ah, corresponding to 74.2% of the rated capacity, owing to internal-resistance-induced voltage drop, electrochemical polarization, and early attainment of the cut-off voltage. The Peukert exponent was estimated to be 1.113. An apparent pack-level direct-current internal resistance (DCIR) of approximately 40.3 mΩ was estimated from the initial voltage-drop analysis under different discharge-current conditions. In addition, the maximum temperature during 10.68C discharge was measured as 55.1 °C, providing a thermal margin of 4.9 °C relative to the operational temperature limit of 60 °C adopted in this study. Finally, a 24S4P battery system with a capacity of 88 Ah, consisting of four 24S1P battery packs connected in parallel, was installed in the VS-210, a 210 kg-class maximum take-off weight (MTOW) eVTOL aircraft. An in-flight test was conducted by repeating six take-off–hovering–landing cycles during a total test session of 15 min 20 s, and a stable propulsion power supply was maintained throughout all flight cycles. This study provides experimental baseline data for the design and preliminary safety assessment of high-power battery systems for UAM applications by presenting both the electrical and thermal characteristics of a 24S NCM battery pack over a wide discharge-rate range of 0.2C–10.68C and in-flight eVTOL data. Full article

Substance use disorders (SUDs) remain a major public health concern and contribute substantially to compromised quality of life, mortality, and healthcare burden. In the United States alone, millions of individuals are affected by alcohol use disorder (AUD), tobacco use disorder (TUD), and opioid use disorder (OUD), with many cases complicated by co-existing anxiety and stress-related disorders. Piper methysticum G. Forst (kava), a traditional South Pacific plant preparation, has gained attention for its anxiolytic, sedative, and sleep-promoting properties. Its pharmacological effects are primarily attributed to a set of lipophilic compounds known as kavalactones, which have been reported to modulate GABA_A receptor activity, dopaminergic and adrenergic signaling pathways, monoamine oxidase-B activity, cannabinoid receptor type 1 activity, and voltage-gated ion channels. Peer-reviewed literature was identified through searches of PubMed, NIH resources, and other scientific databases using terms related to kava, kavalactones, addiction, anxiety, stress, insomnia, and SUDs. Both clinical and preclinical studies were reviewed, including investigations of neurotransmitter systems and addiction-related signaling pathways. The current literature suggests that the strongest rationale for kava use exists in AUD, where anxiety and stress are established contributors to relapse. Evidence supporting kava use in TUD and OUD is largely theoretical, while concerns regarding hepatotoxicity, cytochrome P450 interactions, product variability, and additive risk remain important barriers to its clinical application. In summary, current evidence does not support kava as a replacement for established therapies, while its unique pharmacological profile warrants further investigation as a potential adjunctive treatment for withdrawal and relapse in SUDs. Full article

Extremely fast charging (XFC) of high-energy-density lithium-ion batteries is fundamentally constrained by the intrinsic waveform characteristics of conventional variable-current profiles (VCPs), limiting further reductions in charging time while maintaining electro-thermal safety. In the present study, a computational electro-thermally constrained optimization framework is developed in which a square-wave VCP is reformulated using a finite Fourier series to improve XFC performance. An electro-thermal numerical model is employed to evaluate the charging behavior of the resulting Fourier series-based square wave (F-square wave) with the number of harmonic terms ranging from N = 1 to 100. The optimal charging performance is achieved at N = 10, reducing the charging time from 940 to 878 s (6.6%) and satisfying the U.S. DOE 15-min XFC target (900 s). The performance enhancement originates from two complementary effects: the Gibbs overshoot, which locally increases the charging current near the allowable current limit, and the finite-series approximation, which smooths the current transition before and after the waveform discontinuity. Rather than treating the Gibbs overshoot associated with Fourier approximation as an undesirable numerical artifact, this study demonstrates that it can be computationally exploited as a controlled perturbation to accelerate charging while maintaining electro-thermal safety. Although the Fourier perturbation slightly increases the terminal voltage risk near the waveform discontinuity, all electrical and thermal constraints remain satisfied throughout the charging process. These findings demonstrate that finite Fourier perturbation provides an effective computational design strategy for overcoming the intrinsic waveform limitations of discontinuous charging profiles and advancing electro-thermally constrained XFC of lithium-ion batteries. Full article

Filament heating voltage governs thermionic electron emission in electron-beam guns, but its influence on weld formation under fixed electron-beam welding settings has received limited quantitative investigation. In this study, Ti-6Al-4V thin sheets were welded at filament heating voltages of 2.8–3.4 V, while the accelerating voltage, beam current, focusing current, and welding speed were kept constant. Weld cross-sections were characterized experimentally, and the resulting thermal process was analyzed using a simplified cathode-emission calculation and finite element thermal analysis. A clear change in weld penetration behavior was observed within approximately 3.2–3.3 V. The weld aspect ratio increased from approximately 0.4 below this region to approximately 0.6 at 3.3 V and further to approximately 0.63 at 3.4 V. Concurrent changes in the required bias voltage, calculated equivalent cathode area, and weld geometry were consistent with a change toward a more stable cathode operating condition. The weld-geometry changes were also consistent with a change in the effective beam-energy distribution, although the beam profile was not measured directly. These results show that filament heating voltage should be treated as an independent equipment-side control variable even when the main electron-beam welding settings remain unchanged. Although the specific transition range depends on the electron gun, beam-current setting, and cathode condition, the electrical-response-based identification approach may provide a practical method for identifying the filament operating range when direct beam diagnostics are unavailable. Full article

The contribution of the cathode to system-level efficiency in electrochemical oxidation (EO) is rarely isolated from anodic selectivity limitations, even though its influence on cell voltage, hydrogen evolution kinetics, and energy consumption is well recognized. This study presents a multicriteria methodology that deconvolutes cathode performance from these anodic constraints. A stable lead dioxide anode was paired with three cathodes, carbon felt (CF), stainless steel (SS), and titanium dioxide (TiO₂), for Rhodamine B degradation. The methodology combines conventional electrochemical diagnostics, a ten-parameter multicriteria assessment spanning activity, efficiency, and economics, and a sensitivity analysis prioritizing operational metrics. Application revealed that cathode material governs system-level performance through trade-offs between degradation rate and energy consumption: SS minimized cathodic voltage contribution, while CF maximized degradation rate, with sensitivity analysis confirming CF as the optimal practical choice. However, all systems were constrained by a universal limitation: Faradaic efficiencies remained below 0.3% at an applied current of 30 mA, with anode potential well above the oxygen evolution reaction (OER) threshold and more than 99.7% of charge diverted to unwanted water oxidation. Thus, cathode selection modulates cost and yield but cannot resolve the underlying anodic OER limitation. This methodology offers a transferable diagnostic protocol, indicating that future efforts should prioritize integrated system design over single-electrode optimization to overcome EO selectivity limitations. Full article

Grain boundaries (GBs) are important recombination-active defects in polycrystalline and multicrystalline silicon solar cells, but the effects of their electrical activity, geometry, and spatial arrangement are often difficult to separate. In this work, two-dimensional (2D) drift–diffusion simulations are used to investigate how GB trap density, carrier capture cross-section, orientation, length, number, and network geometry affect silicon solar-cell performance. A controlled comparison between rotating GBs whose length changes with angle and fixed-length GBs shows that the strong apparent orientation dependence is dominated by the accompanying variation in active GB length. When the GB length is fixed at 100 μm, the variations in short-circuit current density ($J_{\mathrm{sc}}$), open-circuit voltage ($V_{\mathrm{oc}}$), efficiency, and fill factor are comparatively small. As a second contribution, irregular polycrystalline microstructures are generated by Voronoi tessellation, producing distributions of grain sizes, shapes, boundary lengths, and junctions that are more representative than simplified structures based on isolated or regularly spaced boundaries. These networks are used to connect grain size, total electrically active GB length, recombination, local electric fields, carrier-flow redistribution, and device performance. As the characteristic grain size increases from 5 to 100 μm, $J_{\mathrm{sc}}$ rises from 15 to $34\mathrm{mA}{\mathrm{cm}}^{-2}$, $V_{\mathrm{oc}}$ from $0.54$ to above $0.61$ V, and the power conversion efficiency from $6.5\%$ to $17\%$. GB-induced photovoltaic loss is therefore governed not by GB number or nominal orientation alone, but by the combined effects of electrical activity, total active boundary length, and network geometry. Full article

Background/Objectives: The demand for reducing iodinated contrast agent (ICA) dose in cerebral CT angiography (CTA) for aneurysm detection has increased due to concerns over patient safety and image quality. The aim of this study is to systematically review the diagnostic accuracy, image quality, and dose efficiency of optimized ICA protocols in CTA for intracranial aneurysm detection. We also aimed to distinguish between direct diagnostic studies reporting aneurysm sensitivity/specificity and indirect technical studies, emphasizing patient-specific tailoring using cardiac output and lean body weight. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science were searched up to 2 June 2026. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUADAS-2 and the NIH tool. Due to clinical and technical heterogeneity, a narrative synthesis was performed. Results: Eleven studies comprising 716 patients were included. Low tube voltage (70–80 kVp) combined with reduced iodine load (8–30 mL, 3.2–9 gI) maintained diagnostic accuracy for aneurysms >3 mm compared to standard protocols. Iterative reconstruction and deep learning improved contrast-to-noise ratio by 23–45% at reduced dose. Technical studies showed feasibility of ultra-low-volume protocols but lacked aneurysm-specific DSA validation. Conclusions: Reduced-contrast and low-kVp CTA protocols may preserve diagnostic performance for intracranial aneurysm detection while lowering iodine exposure. However, current evidence is limited by small sample sizes and heterogeneous protocols. Prospective, aneurysm-specific validation is required before clinical implementation. Full article

Electric motorcycles are central to Sub-Saharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and environmental information, and it predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates high correlation between predicted and measured profiles, with Pearson’s r values of 0.905–0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation. Full article

Improving the efficiency of proton-exchange membrane (PEM) water electrolysis for green hydrogen production requires systematic optimisation of interdependent operating conditions. The present study applies a face-centred central composite design (FCCD) combined with response surface methodology (RSM) to quantify the influence of three controllable parameters on the performance of a bench-scale PEM electrolyser: applied current, stack temperature, and membrane relative humidity. A reversible two-stack configuration with 16 cm² Nafion 117 membrane–electrode assemblies was operated across the design space (0.40–0.90 A, 18–45 °C, 50–100% RH), yielding 288 independent observations from 96 randomised runs. Four responses were evaluated: volumetric hydrogen evolution rate, Faradaic efficiency, specific electrical energy consumption, and stack voltage drift. The regression analysis identified applied current as the dominant factor governing hydrogen throughput, while membrane hydration exerted the strongest control over charge-utilisation and ohmic losses. Temperature exhibited a moderate but statistically significant positive effect, whereas feed-water resistivity emerged as a secondary practical lever for minimising energy consumption. Model adequacy was confirmed through analysis of variance and residual diagnostics, with adjusted coefficients of determination in the range 0.851–0.925 and predicted coefficients above 0.835 across all responses. Desirability profiling indicated an optimal operating window near 0.75 A, 42 °C, and 95% relative humidity, delivering a hydrogen production rate of approximately 7.4 mL min⁻¹, a Faradaic efficiency close to 98%, and a specific energy consumption of 4.5 kWh Nm⁻³. These findings provide quantitative guidance for the design and operation of small-scale PEM electrolysers under constrained laboratory and educational conditions. By integrating formal uncertainty quantification with response surface modelling and jointly treating membrane hydration and feed-water resistivity, the study provides a reproducible, uncertainty-quantified benchmark and a transferable optimisation workflow. Full article

Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses. This paper proposes an integrated solution combining high-precision insulation monitoring and intelligent fault line selection, which ensures the reliability of line selection criteria through improved measurement accuracy and achieves automatic fault isolation via optimized line selection strategies. The paper analyzes the mathematical essence of the ill-conditioned measurement equations of the traditional bridge method under severe single-pole grounding faults, establishes a dual-channel heteroscedastic noise model, and utilizes the inherent physical constraint that the sum of the positive and negative pole-to-ground voltages always equals the bus voltage to transform the ill-posed inverse problem into an equality-constrained optimal estimation problem, deriving an analytical solution in the sense of constrained least squares. A collaborative monitoring strategy of “balanced bridge monitoring first, unbalanced bridge precision measurement afterward” is proposed. An automatic fault line selection and isolation algorithm based on sequential branch switching is designed, which leverages the operational characteristic that PV systems allow short-term branch interruption, enabling automatic identification and isolation of faulty branches and automatic restoration of non-faulty branches without installing any leakage current sensors. Experimental results show that under severe fault conditions with a single-pole insulation resistance as low as 22 kΩ, the proposed method limits the error to within 5%; the proposed line selection strategy can complete identification and isolation of all faulty branches within at most two rounds of switching. Full article