Search Results (142)

This study aims to verify and improve the Electrical Equivalent Circuit Model (EECM) for a regenerated VW e-Golf cell and to develop a verification and optimization framework that enhances simulation accuracy. The model is based on an identified set of EESB elements derived from enhanced measurements of the regenerated cell and is compared with the original cell. A global EESB model is implemented in the PLECS environment, comprising a charge/discharge block and a Voc versus SOC evaluation. Parameters are obtained from measurements of the regenerated VW e-Golf cell and augmented with SOC-dependent polynomial relationships for individual model components. The methodology was applied to identify EESB elements for the regenerated VW e-Golf cell and to produce an EESB model aligned with the identification results. Verification compares simulated and experimental curves in critical SOC regions (0–10%, around 30%, and during relaxation) and cross-validates regenerated versus original cells. Results show that SOC-based polynomial estimates extend the valid range of EESB elements to 0–10% SOC and improve agreement with measured trajectories. Optimization reduces the computational load and improves accuracy, particularly in critical SOC regions, supporting a robust verification framework for regenerated battery cells and guiding further research and implementation in BMS and simulation environments. Full article

Accurate diagnosis of micro short-circuits (MSCs) is essential for ensuring the safety of lithium-ion batteries used in electric vertical take-off and landing (eVTOL) aircraft. Unlike conventional electric vehicles, eVTOL batteries normally operate under high-rate discharge conditions, where strong polarization and rapid voltage variations are apt to mask the weak signatures of MSCs. To address this challenge, this study proposes an MSC diagnosis method based on multiscale voltage residual analysis. A battery model is first established to characterize the normal response under high-rate discharge, and the discrepancy between the measured and estimated terminal voltages is used to construct the model residual. Features describing the overall voltage evolution, residual statistical distribution, and multiscale residual fluctuations are then extracted. Specifically, the shadow region integral area and voltage–capacity slope are used to characterize the global voltage trajectory, while the residual mean and kurtosis quantify the systematic deviation and non-Gaussian fluctuation of the residual. Wavelet decomposition is further applied to capture the low- and high-frequency residual characteristics. After feature reduction, eight representative features are retained to establish the diagnostic model. Experimental validation under high-rate discharge conditions demonstrates that the proposed method can effectively identify MSCs despite interference from abnormal aging, thereby reducing the false alarms caused by feature similarity. This study provides a reliable approach for micro short-circuit diagnosis of eVTOL lithium-ion batteries under strong polarization and highly dynamic operating conditions. Full article

The 550 kV mobile hybrid gas-insulated switchgear (HGIS) has severe difficulties for electromagnetic performance evaluations due to its highly integrated and compact structure. To address this issue, this paper proposes a hierarchical sub-modeling framework. A simplified global model of a 550 kV mobile HGIS retaining the complete main-circuit topology is first established for the macroscopic electrostatic screening of insulation weak zones. Then, high-precision sub-models of the circuit breaker and disconnector switch are then extracted for refined verification. By combining a differentiated meshing strategy with a two-step dynamic solution scheme, transient electric-field evolution of the HGIS is coupled with steady-state and short-circuit transient thermal–fluid simulations. The study reveals that the insulation weak points are explicitly classified into static-structural and dynamic-motion types and identifies that the dynamic closing is the governing condition for insulation verification in the HGIS. The work further shows role reversal of the SF₆ gas between a steady-state overload current and limit short-circuit current condition. The proposed hierarchical modeling for the HGIS significantly reduces the simulation complexity while preserving accuracy, providing efficient analysis guidance and a quantitative basis for the performance validation and structural optimization of compact ultra-high-voltage switchgear. Full article

Structurally integrated lithium-ion batteries (LIBs) in bionic underwater vehicles face increasingly complex internal short circuit (ISC) risks under mechanical abuse. In this study, a three-dimensional bidirectional mechanical–electrical–thermal coupling model is established to reconstruct the evolution from structural damage to ISC triggering in cylindrical LIBs under indentation conditions. A constitutive inversion method incorporating load, contact area, and volume evolution is proposed to calibrate the jellyroll stress–strain response for different indenter diameters. An ISC criterion based on separator thickness is then introduced, and local short-circuit paths are realized through dynamic topology updates of the distributed equivalent circuit model network. The calibrated model reproduced the experimental load response, voltage decay, temperature rise, and damage morphology. The systematic investigation into ISC behavior shows that indenter diameter governs competition among local shear, local bending, and global compression, while loading position determines structural constraint and boundary effects. Rather than corresponding to the minimum ISC load, the most hazardous condition (4 mm indenter diameter and 18 mm loading position) exists where local stress concentration and weakened structural constraints jointly promote rapid separator failure, shortening the ISC triggering time to 79.2 s. These findings provide guidance for battery safety assessment and structural protection design in underwater vehicles. Full article

Skin cancer represents a growing global health burden. Current diagnostic pathways combine clinical examination and dermoscopy with histopathological confirmation; however, overlap between benign and malignant lesions can create diagnostic uncertainty and lead to potentially avoidable biopsies. Electrical impedance spectroscopy (EIS) has emerged as a non-invasive technique with potential for portable and cost-efficient implementation that quantifies the dielectric contrast between malignant and healthy tissue, providing objective information that may support clinical decision-making. This review synthesizes the field across four levels. First, we describe the biophysical origins of the impedance contrast in skin cancer, spanning the cellular, tissue architecture, and molecular scales, together with the equivalent circuit and Cole–Cole frameworks used to interpret it. Second, we examine hardware advances, including electrode–skin interface strategies, flexible and wearable architectures, computational electrode design, and the translation from laboratory prototypes to commercial systems such as Nevisense. Third, we critically appraise clinical evidence from large multicenter trials, focusing on the sensitivity–specificity trade-off and the demonstrated reduction in the number needed to excise. Finally, we discuss emerging frontiers, including artificial intelligence-driven analysis and multimodal fusion with dermoscopy, reflectance confocal microscopy, optical coherence tomography, and near-infrared spectroscopy. We conclude that EIS is most valuable as a complementary component within an integrated, AI-supported multimodal diagnostic framework. Full article

Taking a typical high-power microwave power supply system as the research object, this paper quantitatively simulates and compares electromagnetic disturbance characteristics under multiple operating conditions, systematically investigates the influence mechanism of the system on EMI, and verifies the proposed simulation model via prototype experiments. Firstly, the typical equipment composition and three operating modes of the system are elaborated. Standardized high-frequency equivalent circuits of thyristors, capacitors, and inductors are established, and parasitic parameters are extracted to construct a system-level high-frequency coupling model. Different from traditional static parasitic extraction and separated field-circuit simulation methods, the proposed global collaborative optimization co-simulation method with voltage-dependent thyristor parasitic model significantly improves EMI prediction accuracy under full-cycle multi-mode operation. Secondly, based on the dynamic device characteristics under resonant charging, energy recovery and energy supplement modes, the generation mechanisms of EMI are clarified with quantitative data. During modeling, the electrical characteristics of thyristor body diodes and inter-electrode capacitances are fully incorporated with reference to actual component parameters. The EMC co-simulation based on CST field-circuit coupling is adopted to collaboratively optimize all parameters, which reduces the approximation error introduced by local modeling and greatly improves simulation accuracy. Combined with simulation and prototype experimental verification, this paper reveals the multi-path EMI coupling mechanism of pulsed power systems. The proposed parasitic parameter-based SPICE modeling and field-circuit co-simulation method can provide quantitative analysis tools and theoretical support for the EMC suppression design of high-power microwave power supplies. Full article

Reverse engineering of printed circuit boards is still largely manual when original computer-aided design documentation is unavailable. This paper presents a semi-automatic vision-based pipeline that prepares an editable KiCad schematic draft for use in an Electronic Design Automation (EDA) workflow from paired TOP and BOTTOM board images. The method combines color-profile estimation, pad and through-hole detection, trace segmentation, optical character recognition, component inference, an explicit evidence graph and schematic export with drawn wires. A separate readability step aligns symbols to a grid and reroutes the reconstructed nets with orthogonal wires; it does not change the reconstructed netlist. The primary quantitative evaluation used twelve synthetic KiCad fixtures and three solver configurations: the default sequential pipeline, an opt-in global component solver and an opt-in probabilistic contact solver. These fixtures provide controlled regression cases and are complemented by a small exploratory acquisition trial on real photographed boards. All configurations completed all runs and passed the export round-trip validation without falling back to label-only connectivity. This round-trip check confirms consistency between the internal reconstruction and the exported schematic, but it is reported separately from electrical correctness against the KiCad reference design. The stricter reconstruction-quality criterion still failed on four stress cases involving repeated component chains, long meandering variable-width traces, circular distractors near pads and two-sided transistor layouts. The probabilistic contact solver was therefore kept as an opt-in diagnostic mode rather than enabled by default; it reduced the global pin-to-pin netlist edit distance from 642 to 525 while preserving schematic export checks. The real-board trial indicates that pad and hole detection can transfer to simple photographs, with trace extraction remaining sensitive to uncontrolled illumination and weak copper contrast. The results support the use of the system as a human-in-the-loop reconstruction assistant and identify component grouping, trace-contact reasoning, real-photograph benchmarking and safe missing-edge activation as the main remaining research problems. Full article

This article presents an experimental–computational methodology for evaluating the state of charge (SoC), energy consumption, terminal-voltage behavior, and driving range of a 350 W electric scooter powered by a 36 V, 7.8 Ah lithium-ion battery. The test was carried out using a 117 s elementary urban driving cycle derived from the low-speed section of the New European Driving Cycle (NEDC) and limited to the 32 km/h operating speed of the scooter. Laboratory measurements were performed on rollers under controlled conditions. Battery current and terminal voltage were recorded during the discharge test. The experimental SoC was reconstructed from the measured current by trapezoidal Coulomb counting. The voltage-derived SoC values included in the original laboratory file were kept only for traceability, since they did not correspond to current integration. A MATLAB/Simulink model was developed to reproduce the driving cycle, longitudinal vehicle dynamics, DC motor demand, battery current, and SoC evolution. The valid experimental endpoint occurred at 5233 s, when the terminal voltage reached 31.50 V. At this point, the tested distance was 16.49 km, the discharged capacity was 5.817 Ah, and the final experimental SoC was 25.42%. The simulation produced a discharged capacity of 5.147 Ah and a final SoC of 34.01%, with a charge deviation of 11.51%. Energy consumption was also evaluated from the measured and simulated electrical power. The experimentally integrated discharged energy was 208.10 Wh, equivalent to 12.62 Wh/km. The simulated electrical demand was 184.41 Wh, equivalent to 11.18 Wh/km. A semiempirical terminal-voltage reconstruction, based on the simulated SoC, current demand, an open-circuit-voltage curve, and a fixed internal resistance, reproduced the global voltage-decay trend observed in the experiment. The simplified model captured the general discharge behavior, although it underestimated the measured charge and energy demand. The proposed workflow provides a reproducible basis for comparing manufacturer-declared range, laboratory measurements, current-based SoC reconstruction, energy consumption, and simplified simulation results in light electric vehicles. Full article

A simple electric circuit with memory is discussed, adopting global analysis through fractional analysis. The already presented Fractional analysis with Caputo derivatives, although widely used, fails to satisfy the prerequisites of Differential Topology for a derivative. Hence, they are unable to formulate differential analysis, especially differential geometry. However, the Λ-fractional derivatives can be created for any positive order. In the present analysis, the Λ-fractional analysis is also applied to fractional orders γ with 0 < γ. Although extension of γ is presented for 1 < γ < 2, there is no restriction for extending the present analysis to any 0 < γ. Full article

Wafer-level testing of Photonic Integrated Circuits (PICs) represents a critical throughput bottleneck in silicon photonics manufacturing, particularly as co-packaged optics demand testing of thousands of optical I/O per wafer. This work introduces optimized alignment algorithms for the Technoprobe Eclipse Dynamic probe card system, which integrates electrical probes and a piezoelectrically actuated fiber array unit within a single probe head, eliminating external positioning equipment. We systematically evaluate seven alignment algorithms: Reference Coarse Scan, Reference Coarse+Fine Scan, Cross Scan, Local and Global Bayesian Optimization, Variable and Fixed Gradient Ascent. The evaluation is made across 72 simulated test cases derived from eight experimental datasets through systematic spatial windowing, combined with experimental validation. Performance is assessed under four operating regimes—high-speed (HS) and low-speed (LS) operation, each with or without hysteresis compensation (H/NH). Experimental validation across eight die positions confirms 100% success rate for both Local Bayesian (98.24% accuracy in 99.87 arbitrary units (a.u.)) and Fixed Gradient (99.18% accuracy in 154.01 a.u.) baseline algorithms. Comprehensive simulation results with improved algorithms across all four scenarios reveal distinct performance characteristics. Fixed Gradient achieves the highest reliability (95.8%) with 99.4% average accuracy across all operating conditions. Variable Gradient provides the fastest alignment (1.18 a.u. in HS-NH) with 90.3% reliability. Local Bayesian demonstrates 94.4% reliability with intermediate performance. Global Bayesian Optimization achieves the best sample efficiency (average 24 steps) but exhibits scenario-dependent reliability ranging from 88.9% (HS-H, LS-H) to 93.1% (LS-NH). For the ideal production scenario, high speed with effective hysteresis compensation (HS-NH), Fixed Gradient emerges as the optimal choice, delivering 95.8% reliability with 1.44 a.u. alignment time, resulting in the best success rate while being nearly as fast as the fastest method. Variable Gradient achieves the absolute fastest alignment (1.18 a.u.) but with 5.5% lower reliability (90.3%), making it suitable only for applications tolerating higher failure rates. Under realistic production conditions with uncompensated hysteresis (HS-H), Fixed Gradient maintains its advantage (95.8% reliability, 3.32 a.u.), while Global Bayesian degrades significantly (88.9% reliability, 4.29 a.u.). Statistical analysis using data profiles validates these methods for high-volume PIC manufacturing, with the Eclipse Dynamic system demonstrating per-die optical alignments in sub-second timescales using open-loop control hardware. Full article

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque ($1.34\times {10}^{-4}\%$) and full-load torque ($5.08\times {10}^{-4}\%$). For the previously unreported 25 hp motor, the SSNA yields an objective function value of $4.68\times {10}^{-12}$—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance. Full article

It is projected that, by 2030, the global stock of electric vehicles (EVs) will reach approximately 85 million units. When the capacity of EV batteries declines to 70–80% of their original performance, replacement becomes necessary, as the remaining capacity is inadequate to meet the operational requirements of automotive applications. Upon removal, these batteries retain significant material value and thus require proper recycling. However, their stored energy presents substantial safety risks, necessitating a controlled discharge process to mitigate potential hazards. This study presents the design and implementation of a system that integrates a boost converter with a single-phase grid-tied inverter to facilitate the safe transfer of energy from end-of-life (EoL) EV batteries to the electrical grid. The system was simulated in PLECS using a lithium-ion battery model and a non-ideal grid. The analysis shows that the system is stable and effective at transferring energy from the battery to the grid and heating the battery at the end of the process. This study identifies circuit operating conditions and control schemes that can enable the rapid, practical, and safe discharge of EV batteries without significant voltage relaxation. Full article

As a green energy technology, triboelectric nanogenerators (TENGs) convert mechanical energy into electricity and have gained significant attention in response to growing global environmental concerns. However, the widespread use of petroleum-based polymers as triboelectric materials in high-performance TENGs raises concerns over plastic pollution. In this work, we report a high-performance biodegradable TENG utilizing chitosan/laser-induced graphene (LIG) composite films as triboelectric layers. Modified chitosan substrates were first converted into LIGs via a convenient one-step CO₂ laser engraving, subsequently incorporated into chitosan matrices to form homogeneous composite films. A TENG device was designed by pairing the LIG/chitosan composite film with the fluorinated ethylene propylene (FEP) film, and copper electrodes. The introduction of LIG effectively strengthens charge storage and dielectric properties of the chitosan matrix, thereby significantly boosting the triboelectric output performance. Experimental results demonstrate that the as-assembled TENG with an LIG concentration of 1 wt.% achieves a peak open-circuit voltage of 196 V and short-circuit current of 2.1 μA, with a maximum power density of 295 mW/m². It can drive LED lights and small low-power electronic devices. Furthermore, the designed TENG device exhibits good biodegradability, flexibility, and stability, serving as a self-powered sensor for monitoring human joint movements. This work provides a simple and scalable strategy for integrating laser-induced graphene with biomass-based polymers, offering new insights into the design of high-performance, biobased triboelectric materials. Full article

Dry-type electrical transformers are essential components in commercial, industrial, and residential power distribution systems, as they adapt voltage levels required by a broad range of load types. Although they are robustly constructed, they are exposed to adverse operational and environmental conditions such as dust, humidity, and electrical disturbances that may cause premature winding damage, such as inter-turn short circuits. This study focuses on the detection of inter-turn short-circuit faults in a 15 kVA commercial dry-type transformer, where a fault equivalent to 11.54% of short-circuited turns was induced in the tap changers. Axial, radial, and rotational leakage magnetic flux signals were captured using a low-cost, non-invasive triaxial Hall-effect magnetic flux sensor. During data processing, Fisher Score feature selection was applied to identify the most relevant indicators. Subsequently, feature extraction techniques, including Linear Discriminant Analysis, Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection, and Isometric Mapping, were evaluated. The technique that best preserved global and local data structures was selected using Trustworthiness, Spearman’s correlation, and Kruskal’s stress metrics. PCA was selected as the optimal technique based on these quality metrics, achieving the highest classification performance. The resulting subspace data were classified using support vector machines and applying K-fold cross-validation. The proposed system achieved classification accuracies above 95%, with high recall and F1-score values, for inter-turn fault detection in each winding, confirming its effectiveness for reliable inter-turn fault detection in each transformer winding. Full article

Diabetes, projected to affect over 1.3 billion people by 2050, presents significant healthcare burdens and environmental challenges, necessitating innovative and sustainable solutions to manage complications effectively. This study applies life cycle assessment to evaluate the environmental impacts of two semiconductor-enabled diabetes care devices: (1) a single-use urine-based C-peptide measurement strip aligned with the reduce strategy and (2) a reusable smart wound dressing for chronic wound monitoring under the reuse strategy. Integrating green electricity reduced the total lifecycle global warming potential by 16.2% for the urine strip and 0.4% for the smart wound dressing. The results emphasize the importance of tailored design strategies, showing that the impact of green integrated circuits is substantial for single-use reduce systems, while long-term treatments benefit more from reuse strategies paired with durable, complex designs that extend component lifespan and limit new manufacturing burdens. Full article