Search Results (2,648)

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

Drilling resistance (DR) measurement is a promising non-destructive technique for evaluating the mechanical properties of concrete. However, the reliability and repeatability of DR measurements are still limited by an insufficient understanding of how drill rotational speed influences the recorded drilling response. In addition, a systematic investigation of the influence of rotational speed on multiple drilling response parameters simultaneously is still lacking. This study investigates the relationship between imposed rotational speed and DR parameters—namely, rotational speed reduction, drilling force, and electrical power consumption—measured during controlled drilling tests in C30 and C50 concretes. A laboratory-developed DR testing methodology with constant feed rate and synchronized RPM, force, and power measurements was applied. Five nominal drilling speeds (in the range of 1400–2200 RPM) were examined. The results show clear, speed-dependent trends across all measurements. Strong correlations between nominal and in-hole rotational speeds were observed, while drilling force exhibited a nonlinear dependence on rotational speed. This study reveals distinct drilling behavioral signatures that differentiate concrete strength classes and clarify the mechanical origin of drilling-induced RPM reduction. The findings confirm that DR parameters, when analyzed collectively rather than individually, provide valuable diagnostic information and have strong potential for application in the non-destructive evaluation of concrete structures. Full article

A method for the second-harmonic generation (SHG) in optical fibers by exploiting the third-order nonlinearity under an external electric field is proposed. The analysis begins with the electric polarization vector of the SHG, and the analytical solution for the SHG is presented. When fiber birefringence is neglected, a mode-field matching condition is introduced. The nonlinearity-induced shift in propagation constant is provided based on Gaussian approximation. For a specific case, the power of SHG is calculated. The results show that the SHG power scales quadratically with the nonlinear coefficient. Reducing the effective area of the fiber and increasing the nonlinear coefficient can enhance the SHG power by 1–2 orders of magnitude. Since phase matching strongly affects the SHG process, optimizing the fiber design is crucial. Additionally, the polarization state of SHG is shown to have the same as the equivalent optical field of the injected fundamental wave. This work demonstrates potential for distributed sensing of electric fields and lightning events in high-voltage power grids using optical fibers. Full article

In this paper, an anisotropic object electromagnetic image reconstruction system based on a two-stage multi-frequency extended network is developed by deep learning techniques. We obtain the scattered field information by irradiating the TM different polarization waves to uniaxial objects located in free space. We input the measured single-frequency scattered field into the Deep Residual Convolutional Neural Network (DRCNN) for training and to be further extended to multi-frequency data by the trained model. In the second stage, we feed the multi-frequency data into the Deep Convolutional Encoder–Decoder (DCED) architecture to reconstruct an accurate distribution of the dielectric constants. We focus on EMIS applications using Transverse Magnetic (TM) and Transverse Electric (TE) waves in 2D scenes. Numerical findings confirm that our method can effectively reconstruct high-contrast uniaxial objects under limited information. In addition, the TM/TE scattering from uniaxial anisotropic objects is governed by polarization-dependent Lippmann–Schwinger integral equations, yielding a nonlinear and severely ill-posed inverse operator that couples the dielectric tensor components with multi-frequency field responses. Within this mathematical framework, the proposed two-stage DRCNN–DCED architecture serves as a data-driven approximation to the anisotropic inverse scattering operator, providing improved stability and representational fidelity under limited-aperture measurement constraints. Full article

Electrical curing is a viable alternative to traditional thermal curing for geopolymer materials due to its capability for rapid and internal geopolymerization. In this research, reinforced geopolymer-based composite beams were successfully fabricated at a macroscale using a binary system of fly ash (FA) and granulated blast furnace slag (GBFS). The mixture was activated with a solution of sodium silicate (Na₂SiO₃) and sodium hydroxide (NaOH) with a fixed molar ratio of 2:1 for both, and aggregate-to-binder and activator-to-binder (A/B) ratios of 2.5 and 0.7, respectively. To ensure electrical conductivity, individual fiber systems were employed, including carbon fiber (CF), steel fiber (SF), and waste wire erosion (WWE), each incorporated at a dosage of 0.5 vol.% of the total mix volume. In addition, carbon black (CB) was introduced as a conductive filler at a constant dosage of 2.0 vol.% of the binder content in selected specimens. Each beam specimen contained only one type of conductive reinforcement or filler. A total of twelve reinforced geopolymer-based composite beams with a 150 mm square section and a span of 1300 mm, with a clear span of 1200 mm, were successfully cast and reinforced based on reinforced concrete beam designs and standards, with a dominant goal of enhancing beam behavior under flexure. The beams were cured in ambient curing conditions, or using thermal curing at 80 °C for 24 h, and using electrical curing from the fresh states with a fixed voltage of 25 V. Notwithstanding a common beam size and reinforcement pattern, distinct curing methods significantly influenced beam structure properties. Peak loads were between 20.8 and 31.5 kN, initial stiffness between 1.75 and 6.09 kN/mm, and total energy absorption between 690 and 1550 kN/mm, with a post-peak energy component of between 0.12 and 0.55. Displacement-based ductility measures spanned from 3.2 to 8.1 units with a distinct improvement in electrical curing regimes, especially in the SF-reinforced specimens; this indicates that electrical curing in reinforced geopolymer composite materials works as a governing mechanism in performance rather than simply a method for enhancing the strength of materials. Full article

Ionic polymer–metal composite (IPMC) sensors generate voltages or currents when subjected to deformation. The magnitude and time constant of the electrical response vary significantly with ambient humidity and water content. However, most conventional physical models focus solely on cation dynamics and do not consider water dynamics. In addition to cation dynamics, Zhu’s model explicitly incorporates the dynamics of water. Consequently, Zhu’s model is considered one of the most promising approaches for physical modeling of IPMC sensors. This paper presents exact analytical solutions to Zhu’s model of IPMC sensors for the first time. The derivation method transforms Zhu’s model into the frequency domain using Laplace transform-based analysis together with linear approximation, and subsequently solves it as a boundary value problem of a set of linear ordinary differential equations. The resulting solution is expressed as a transfer function. The input variable is the applied bending deformation, and the output variables include the open-circuit voltage or short-circuit current at the sensor terminals, as well as the distributions of cations, water molecules, and electric potential within the polymer. The obtained transfer functions are represented by irrational functions, which typically arise as solutions to a system of partial differential equations. Furthermore, this paper presents analytical approximations of the step response of the sensor voltage or current by approximating the obtained transfer functions. The steady-state and maximum values of the time response are derived from these analytical approximations. Additionally, the relaxation behavior of the sensor voltage is characterized by a key parameter newly derived from the analytical approximation presented in this paper. Full article

A flexible piezoresistive material based on vulcanized natural rubber (VNR) and multiwalled carbon nanotubes (MWCNTs) was developed and systematically investigated for strain sensing applications. The nanocomposites were prepared by melting and vulcanizing MWCNT, while keeping the rubber composition constant to isolate the effect of the conductive nanofiller. By scanning electron microscopy, morphological analyses indicated that MWCNTs were dispersed throughout the rubber matrix, with localized agglomerations becoming more evident at higher loadings. In mechanical tests, MWCNT incorporation increases the tensile strength of VNR, increasing the stress at break from 8.84 MPa for neat VNR to approximately 10.5 MPa at low MWCNT loadings. According to the electrical characterization, VNR-MWCNT nanocomposite exhibits a strong insulator–conductor transition, with the electrical percolation threshold occurring between 2 and 4 phr. The dc electrical conductivity increased sharply from values on the order of 10⁻¹⁴ S·m⁻¹ for neat VNR to approximately 10⁻³ S·m⁻¹ for nanocomposites containing 7 phr of MWCNT. Impedance spectroscopy revealed frequency-independent conductivity plateaus above the percolation threshold, indicating continuous conductive pathways, while dielectric analysis revealed strong interfacial polarization effects at the MWCNT–VNR interfaces. The piezoresistive response of samples containing MWCNT exhibited a stable, reversible, and nearly linear response under cyclic tensile deformation (10% strain). VNR/MWCNT nanocomposites demonstrate mechanical compliance and tunable electrical sensitivity, making them promising candidates for flexible and low-cost piezoresistive sensors. Full article

The development of flexible and self-powered electronic systems requires triboelectric materials that combine high charge retention, mechanical compliance, and stable dielectric properties. Here, we report a redox reaction approach to prepare liquid metal particle-reduced graphene oxide (LMP@rGO) core–shell structures and introduce into a poly(acrylic acid) (PAA) hydrogel to create a high-performance triboelectric layer. The spontaneous interfacial reaction between gallium oxide of LMP and graphene oxide produces a conformal rGO shell while simultaneously removing the native insulating oxide layer onto the LMP surface, resulting in enhanced colloidal stability and a controllable semiconductive bandgap of 2.7 (0.01 wt%), 2.9 (0.005 wt%) and 3.2 eV (0.001 wt%). Increasing the GO content promotes more complete core–shell formation, leading to higher zeta potentials, stronger interfacial polarization, and higher electrical performance. After embedding in PAA, the LMP@rGO structures form hydrogen-bonding networks with the hydrogel nature, improving both dielectric constant and charge retention while notably preserving soft mechanical compliance. The resulting LMP@rGO/PAA hydrogels show enhanced triboelectric output, with the 2.0 wt/vol% composite generating sufficient power to illuminate more than half of 504 series-connected LEDs. All the results demonstrate the potential of LMP@rGO hydrogel composites as promising triboelectric layer materials for next-generation wearable and self-powered electronic systems. 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 examines the effects of temperature, relative humidity, moisture content, and density on the dielectric constant (ε′) and dielectric loss tangent (tan δ) of oak wood lamellae within a frequency range of 0.079 MHz to 25.1 MHz. The hypothesis tested was that increased temperature and moisture content enhance both dielectric polarization and loss, while density acts as a dominant structural determinant of dielectric behaviour. Oak lamellas were conditioned above saturated salt solutions at 20 °C and measured using an Agilent 4285A LCR meter according to ASTM D150-22. Multiple linear regression was used to demonstrate the statistically significant influence of temperature, relative humidity, moisture content, and density on the tested electrical properties of the lamellas. The results showed that the dielectric properties increase with higher sample density and higher air humidity. Temperature also had an influence, but it was significantly smaller, though still statistically significant (p < 0.05). Changes in dielectric properties were most pronounced at frequencies below 1 MHz, suggesting that dipolar and interfacial polarization are greater at lower frequencies. The findings in this paper provide a basis for optimizing the high frequency/dielectric heating process for heating before bending of oak and other similar hardwoods. Full article

Numerical simulation of the electrical conductivity of carbon fiber-reinforced asphalt concrete is essential for understanding its electrical behavior, yet research in this area remains limited. This study prepared six groups of Marshall specimens with carbon fiber (CF) contents of 0.1 wt%, 0.2 wt%, 0.3 wt%, 0.4 wt%, 0.5 wt%, and 0.6 wt%. The resistivity and asphalt concrete (AC) impedance spectra were measured to analyze the effect of fiber content on electrical performance. Nyquist diagrams were fitted to establish an equivalent circuit model, and a representative volume element (RVE) finite element model was developed. The Generalized Effective Medium (GEM) equation was employed to fit the resistivity data. The results show that the resistivity exhibits a two-stage characteristic—an abrupt decrease followed by stabilization, with an optimal CF content range of 0.2–0.4 wt%. Among the equivalent circuit parameters, the contact resistance (R₁) and tunneling resistance (R₂) significantly decreased, the growth of interface capacitance (C₁) slowed, the constant phase element Z_Q increased, and the non-monotonic change of volume resistance (R₃) reflected the heterogeneity of the internal void distribution of the material. The finite element numerical solution for resistivity, derived from the GEM equation, aligns well with experimental values, validating the proposed simulation approach. Full article

This paper proposes a regional inertia estimation method in power systems using ambient data measured by phasor measurement units (PMUs). The proposed method employs band-pass filtering to suppress the low-frequency influence of mechanical power and to attenuate high-frequency noise and discrepancies between rotor speed and electrical frequency. By utilizing a simple first-order AutoRegressive Moving Average with eXogenous input (ARMAX) model, this process allows the inertia constant to be directly identified. This method requires no prior model order selection, rotor speed estimation, or computation of the rate of change of frequency (RoCoF). The proposed method was validated through simulation on three benchmark systems: the Kundur two-area system, the IEEE Australian simplified 14-generator system, and the IEEE 39-bus system. The method achieved area-level inertia estimates within approximately ±5% error across all test cases, exhibiting consistent performance despite variations in disturbance models and system configurations. The estimation also maintained stable performance with short data windows of a few minutes, demonstrating its suitability for near real-time monitoring applications. Full article

The optimization of battery electric vehicles requires advanced high-strength steels that combine ductility and toughness, enabling lightweight leaf spring constructions with improved performance. This study investigates processing optimization by comparing the newly developed 45SiCrV9Ni, previously identified as promising for stress peening and fatigue, with the conventional 51CrV4 as a benchmark. Dilatometric, mechanical, and microstructural analyses were conducted in as-supplied and heat-treated conditions. Both steels show excellent high-temperature ductility, making them suitable for hot forming under similar conditions. However, 45SiCrV9Ni requires a higher temperature for homogenized austenitization. After tempering, it consistently exhibits superior hardness and toughness compared to 51CrV4. Importantly, its ductility remains nearly constant over a wide tempering temperature range, allowing lower ones to be chosen without compromising strength or toughness, offering additional energy-saving possibilities. These results highlight the potential of 45SiCrV9Ni for leaf spring applications. Full article