Search Results (2,207)

A compact bio-inspired terahertz wideband antenna is presented in this work. The proposed antenna is based on Viburnum tinus leaf shape, a defective ground plane, a folded-ring slot, and parasitic elements. The footprint of the proposed antenna is $0.46\times 0.18{\lambda }_g^2$ at 0.18 THz. A bandwidth of 0.536 THz (0.18–0.72 THz) is achieved with a band notch at 0.35 THz (0.3–0.36 THz). The proposed antenna has a peak gain of 5 dBi and the stable radiation patterns. The proposed antenna is validated through a finite difference time domain simulator and the equivalent circuit analysis. The results from show a good correlation. Also, an extensive parametric analysis is performed, and the comparative analysis of the proposed antenna with the existing antennas shows that the proposed antenna is compact with competitive performance metrics such as gain, efficiency, and notch-band characteristics. Therefore, the proposed antenna (hereafter referred to as VTB-A) is a promising candidate for future terahertz wireless communications (5G, 6G, and beyond) and terahertz imaging. Full article

To address the micro-vibration isolation requirements of precision payloads in spacecraft, a Lorentz force-based magnetic levitation series vibration isolation platform is proposed. The Lorentz force actuator, overall coupling characteristics, and low-frequency vibration isolation performance of the platform are optimized, simulated, and experimentally validated. During the actuator design phase, an equivalent magnetic circuit model and an equivalent current model are established for the planar actuator. The theoretical relationship between magnetic flux density in the air gap and magnetization length is derived. Through finite element simulation, the optimal magnetization length is determined to be 7 mm. For the coupling analysis, a dynamic model of the platform is developed to quantify the coupling effects between translational and rotational motions. To evaluate the low-frequency vibration isolation performance, sinusoidal displacement at various frequencies is applied to emulate the space vibration environment and validate the isolation capability. The results show that the platform has low translational-rotational cross-coupling, and the vibration transmissibility of low-frequency micro-vibration is less than 35 dB. This system offers a high-precision, low-coupling solution for vibration isolation in precision optical instruments. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles (EVs). Conventional approaches, including Coulomb counting, electrochemical impedance spectroscopy, and equivalent circuit models, provide useful insights but face practical limitations such as error accumulation, high equipment requirements, and limited applicability across different conditions. These challenges have encouraged the use of machine learning (ML) methods, which can model nonlinear relationships and temporal degradation patterns directly from cycling data. This paper reviews four machine learning algorithms that are widely applied in SOH estimation: support vector regression (SVR), random forest (RF), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Their methodologies, advantages, limitations, and recent extensions are discussed with reference to the existing literature. To complement the review, MATLAB-based simulations were carried out using the NASA Prognostics Center of Excellence (PCoE) dataset. Training was performed on three cells (B0006, B0007, B0018), and testing was conducted on an unseen cell (B0005) to evaluate cross-battery generalisation. The results show that the LSTM model achieved the highest accuracy (RMSE = 0.0146, MAE = 0.0118, R² = 0.980), followed by CNN and RF, both of which provided acceptable accuracy with errors below 2% SOH. SVR performed less effectively (RMSE = 0.0457, MAPE = 4.80%), reflecting its difficulty in capturing sequential dependencies. These outcomes are consistent with findings in the literature, indicating that deep learning models are better suited for modelling long-term battery degradation, while ensemble approaches such as RF remain competitive when supported by carefully engineered features. This review also identifies ongoing and future research directions, including the use of optimisation algorithms for hyperparameter tuning, transfer learning for adaptation across battery chemistries, and explainable AI to improve interpretability. Overall, LSTM and hybrid models that combine complementary methods (e.g., CNN-LSTM) show strong potential for deployment in battery management systems, where reliable SOH prediction is important for safety, cost reduction, and extending battery lifetime. Full article

The lithium-ion cell model is the heart of the battery management system—a more accurate model ensures operational safety, extends pack lifetime, and provides better tracking of battery charge and health. Catalyzed by the automotive industry’s shift towards electrification, optimal parameterization of the lithium-ion cell is of crucial importance. Two dominant methods of direct parameterization have emerged in the literature as the standard for parameter extraction of a lithium-ion equivalent circuit cell model. A direct comparison of their performance and suggestion as to the optimal method of cell parameterization has not yet been proposed; Thus, this paper aims to extract the optimal parameter set regarding the two dominant direct methods with an electrochemically based logic, compare the accuracy of cell parametrization over two transient load profiles, and ultimately suggest which method is preferable for electric vehicle applications. Additionally, this work will be conducted over multiple C-rates to quantify the robustness of each direct method of parameterization over a transient load profile. Full article

The global adoption of solar-powered streetlights has grown significantly, driven by their cost-effectiveness and potential to reduce dependence on fossil fuels associated with conventional street lighting. Battery storage represents a substantial portion of the total capital cost in solar-powered streetlight systems. Therefore, selecting an efficient charge regulator is crucial to protect battery lifespan and reduce energy losses. In this context, the choice of an appropriate charge regulator plays a vital role in enhancing system reliability and overall performance. This study presents a practical approach for evaluating three commercially available 6 A-rated Pulse Width Modulation (PWM) solar charge regulators intended for recharging lead-acid batteries in a proposed 12 V off-grid solar photovoltaic (PV) street lighting system. The regulators were evaluated concurrently in separate circuits, each experiencing similar meteorological conditions, including similar temperature and solar irradiance. The measured data for each regulator were acquired using LabVIEW-based virtual instruments. The performance comparison was conducted using the Linear Regression Algorithm (LRA) to support decision-making. Based on the analysis, the most suitable PWM charge regulator was identified as the one offering the best charging performance due to low internal losses. Hence, solar battery charge regulators with identical load current ratings do not necessarily deliver equivalent charge/discharge performance. Full article

Micro-photosynthetic power cells (μPSCs), also known as biophotovoltaics (BPVs), represent sustainable and self-regenerating solutions for harvesting electricity from photosynthetic microorganisms. However, their practical deployment has been constrained by low voltage, low current output, and scaling inefficiencies. In this work, we address these limitations through a dual-optimization strategy: (i) systematic quantification of how electrode surface area influences key performance metrics, and (ii) based on our previous work we highlighted the novel hybrid modular array architectures that combine series and parallel connections of μPSCs. Three single μPSCs with electrode areas of 4.84, 19.36, and 100 cm² were fabricated and compared, revealing that while open-circuit voltage remains largely area-independent (850–910 mV), both short-circuit current and maximum power scale with electrode size. Building on these insights, two hybrid array configurations fabricated from six 4.84 cm² μPSCs achieved power outputs of 869.2 μW and 926.4 μW, equivalent to ~82–87% of the output of a large 100 cm² device, while requiring only ~29% electrode area and ~70% less reagent volume. Importantly, these arrays delivered voltages up to 2.4 V, significantly higher than a single large device, enabling easier integration with IoT platforms and ultra-low-power electronics. A meta-analysis of over 40 reported BPV/μPSC systems with different electrode surface areas further validated our findings, showing a consistent inverse relationship between electrode area and power density. Collectively, this study introduces a scalable, resource-efficient strategy for enhancing μPSC performance, providing a novel design paradigm that advances the state of the art in sustainable bioenergy and opens pathways for practical deployment in distributed, low-power and IoT applications. Full article

This paper simplifies indirect resonant switched-capacitor (ReSC) converters using the state-space average model method. The operation principles of the 4:1 and 5:1 ReSC converters derived from the Dickson (4:1) circuit are analyzed, and the corresponding state-space average matrices are derived based on their equivalent circuits. The resonant inductor of the specific resonant branch is eliminated by analyzing the composition of the state-variable matrix, thereby obtaining the simplified topologies of 4:1 and 5:1 indirect ReSC converters. The simplified topologies are simulated and experimentally verified. The results prove the correctness of the state-space average modeling method and the effectiveness of the simplified topologies. Full article

The conversion of low-grade heat into storable electrical energy using nanoporous carbon materials represents an efficient energy harvesting strategy. In this study, a reduced graphene oxide (RGO) and carbon nanotube (CNT) composite with a rich microporous structure was synthesized. A symmetrical thermoelectric cell was constructed to harvest thermal energy. The application of a temperature difference (ΔT) generated a stable equilibrium voltage (U_s), which scaled linearly with ΔT. The resulting thermoelectric coefficient (U_s/ΔT) increased markedly with the carbon nanotube (CNT) content, underscoring the effectiveness of CNT incorporation for improving thermoelectric properties. It also shows a non-monotonic dependence on KCl concentration, first increasing and then decreasing, with a maximum value of 4.17 mV/°C achieved in 0.1 M KCl using the RGO-5%CNTs electrode. When connected to an external load, the discharge voltage and current decay rapidly before stabilizing within seconds. Circuit analysis reveals that the incorporation of CNTs reduces internal resistance and increases the equivalent capacitance. Although instantaneous discharge power declines quickly, the addition of CNTs elevates its initial value and slows the decay rate. Both the average output power and thermoelectric conversion efficiency improve with increasing ΔT and are further enhanced at higher CNT content. Overall, the RGO-CNT composite demonstrates significantly superior thermoelectric performance compared to pure RGO. Full article

This paper proposes a solution to enhance the torque production capability of Permanent Magnet Synchronous Machine (PMSM), utilizing not only the unused space resulting from the stator end windings on the rotor side, but also the otherwise unused space around the winding on the yoke side. By implementing an additional axial rotor equipped with Permanent Magnets (PMs) in both rotor and yoke sides, the proposed design technique increases the PMSM torque output, taking advantage of the useless space on the yoke side. In the proposed configuration, one magnetic flux path circulates between the PMs on the rotor (rotor side) and the stator, while an additional flux path circulates between the PMs positioned on both sides of the stator end windings. These two flux paths contribute to generating a stronger and more effective magnetic field within the machine than conventional structure, resulting in increased torque density. A magnetic equivalent circuit (MEC) model of the proposed design is developed, and its accuracy is validated through Finite Element (FE) analysis. For a fair evaluation, the proposed structure is compared with a conventional configuration using the same volume of PM material. Furthermore, optimization of the proposed design is carried out to maximize Torque/PM. Full article

Dielectric characterization offers valuable insights into fruit structure, ripening, and storage stability. However, systematic studies on apples are still limited. This work elucidates the electrical and physicochemical properties of a specific variety of apples, Malus domestica, using Electrochemical Impedance Spectroscopy (EIS), a non-destructive, fast and cost-effective technique, suitable for real-time quality assessments. The apple samples were analyzed over the frequency range of 20 Hz–120 MHz at 25 °C, and impedance data were modeled using equivalent circuits and dielectric relaxation models. Physicochemical analyses confirmed a high moisture content (84%, wwb), pH 4.81, TSS 14.58 °Brix, and acidity 0.64%, which is typical of fresh Red Delicious apples. Impedance spectra revealed semicircular and Warburg elements in Nyquist plots, indicating resistive, capacitive, and diffusive processes. Equivalent circuit fitting with the proposed R-C-Warburg impedance model outperformed (R² = 0.9946 and RMSE = 6.610) the classical Cole and Double-Shell models. The complex permittivity (ε) represented a frequency-dependent ionic diffusion, space-charge polarization, and dipolar relaxation decay, while electrical modulus analysis highlighted polarization and charge carrier dynamics. The translational hopping of charge carriers was confirmed through AC conductivity following Jonscher’s power law with an exponent of ƞ = 0.627. These findings establish a comprehensive dielectric profile and advanced circuit fitting for biological tissues, highlighting a promising non-invasive approach using EIS for real-time monitoring of fruit quality, with direct applications in post-harvest storage, supply chain management, and non-destructive quality assurance in the food industry. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

Due to the highly nonlinear, dynamic, and slowly time-varying nature of lithium-ion batteries (LIBs) during operation, achieving accurate and real-time parameters online identification in first-order RC equivalent circuit models (ECMs) remains a significant challenge, including low accuracy and poor real-time performance. This paper establishes a fractional-order chaotic system for first-order RC-ECM based on a charge-controlled memristor. The system exhibits chaotic behavior when parameters are tuned. Then, based on the principle of the state observer, an identification observer is designed for each unknown parameter of the first-order RC-ECM, achieving online identification of these unknown parameters of the first-order RC-ECM of LIB. The proposed method addresses key limitations of traditional parameter identification techniques, which often rely on large sample datasets and are sensitive to variations in ambient temperature, road conditions, load states, and battery chemistry. Experimental validation was conducted under the HPPC, DST, and UDDS conditions. Using the actual terminal voltage of a single cell as a reference, the identified first-order RC-ECM parameters enabled accurate prediction of the online terminal voltage. Comparative results demonstrate that the proposed state observer achieves significantly higher accuracy than the forgetting factor recursive least squares (FFRLS) algorithm and Kalman filter (KF) algorithm, while offering superior real-time performance, robustness, and faster convergence. Full article

Inter-turn short-circuit faults in power transformers generate enormous short-circuit currents within the affected turns, making full-scale experimental investigations impractical. To address this issue, this study proposes an experimental method utilizing a third external short-circuit winding to simulate inter-turn faults through structural improvements in winding configuration and conductor current-carrying capacity. A simulation calculation model for transformer inter-turn short circuits was first established to investigate the equivalence between the proposed equivalent fault model and actual fault conditions under varying short-circuit positions and proportions. Simulation results demonstrate that both models exhibit consistent primary/secondary winding currents, short-circuit turn currents, and spatial radial leakage magnetic field distributions post-fault, with average errors less than 5%. Subsequently, an experimental platform for inter-turn short-circuit fault simulation was constructed. Current and leakage magnetic field measurements under different fault positions and proportions were validated against simulation data, confirming the proposed method’s equivalence. This approach provides an effective pathway for investigating fault characteristics and monitoring methodologies of transformer inter-turn short circuits. Full article

Quantum-dot Cellular Automata (QCA) technology has emerged as a promising approach for constructing nanoscale digital circuits, offering notable advantages such as minimal power consumption, rapid processing speeds, and highly compact layouts. Traditional CMOS technology faces significant challenges at the nanoscale, including reduced gate control and increased current leakage. QCA, on the other hand, provides a robust platform for building next-generation digital systems. In this study, a unique single-layer QCA-based Full-Adder (QCAFA) and Carry-Save Adder (CSA) architecture is developed to enhance key performance factors such as delay, space, cost, and cell block count. The outlined designs demonstrate superior efficiency compared to state-of-the-art single-layer and multilayer QCA designs. Simulation results conducted with QCADesigner 2.0.3 and QCADesigner-E reveal that the proposed architecture achieves a substantial 34.29% diminution in total cells compared with the recent design, utilising only 46 QCA cells. Similarly, for the CSA, the proposed design attains an 18.62% reduction in cell count compared with its best counterpart, utilising only 424 QCA cell blocks. To enhance design credibility and hardware relevance, this research additionally models and validates the architecture using the Verilog hardware description language (HDL Version 12.0), thereby bridging the gap between nano-architecture and HDL-based prototyping. Simulation results obtained through QCADesigner confirm the correctness and stability of the QCA layout, while HDL simulation verifies functional equivalence at the behavioural and structural levels. The proposed designs not only enhance speed and reduce energy consumption but also offer better manufacturability. The findings of this study highlight the potential of QCA technology as a feasible substitute for CMOS for high-performance digital arithmetic circuits at the nanoscale. Full article

We present the design of an optically transparent, flexible, and tunable microwave absorber covering the X and Ku frequency bands. The absorber is based on a metasurface composed of a periodic array of graphene spiral resonators (GSRs) attached to an ultrathin PET film placed over an ITO-backed dielectric spacer. An equivalent circuit model (ECM), described by closed-form equations, is proposed to optimize the structure for maximum absorption within the target frequency range. The optimized absorber achieves a peak absorbance of 99.7% for normally incident waves while maintaining over 90% absorption at various incident angles in the frequency range from 8.5 GHz to 17.4 GHz. In addition, a double-layer graphene spiral resonator (DGSR) metasurface is proposed to extend the absorber’s operational bandwidth, demonstrating a bandwidth enhancement of approximately 3 GHz and a relative bandwidth of 90% without compromising miniaturization or incident angle stability. Given their remarkable attributes, both GSR and DGSR configurations show great potential for applications in radar stealth technology and transparent electromagnetic compatibility. Full article