Search Results (1,648)

Peripheral nerve injuries pose a significant clinical challenge due to the limited self-repair capacity and the complexity of neural tissue architecture. Tissue engineering strategies applied to the peripheral nerve system aim to restore functional nerve constructs by combining scaffolds, cells, and biochemical cues to recreate the native microenvironment. This work aimed to propose the electrical conductivity as a functional readout of structural and biological remodeling in engineered peripheral nerve scaffolds, along with functional and molecular evaluations. To this end, bioimpedance measurements were combined with equivalent circuit modeling to track state-dependent changes across different levels of tissue organization. Murine sciatic nerves were decellularized and recellularized with neural populations to generate engineered constructs, and their electrical properties were assessed using broadband bioimpedance spectroscopy. Distinct impedance profiles were observed across control, decellularized, and recellularized samples, reflecting structural and functional changes associated with cell removal and repopulation. Furthermore, a multilayer series RC circuit model was implemented to accurately reproduce the measured spectra, enabling the extraction of layer-specific electrical parameters. Analysis of these parameters revealed that decellularization reduces compartmental resistances and increases inter-layer coupling, whereas recellularization restores outer-layer resistances and reduces coupling, consistent with functional tissue organization. Overall, the results demonstrate that bioimpedance provides a readout of the scaffold biological state and cellular integration, and that equivalent circuit modeling offers a quantitative framework to link structural remodeling to electrical function in engineered peripheral nerve tissues. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is one of the core functions of a battery management system and is of great significance for ensuring the safe operation of electric vehicles and optimizing energy utilization. However, due to the strong nonlinearity, time-varying characteristics, and interference from complex operating conditions within the battery, high-precision SOC estimation faces severe challenges. To address the problems that a single data-driven method lacks physical constraints and a single model-driven method struggles to characterize complex nonlinearities, this paper proposes a series-connected LSTM-UKF fusion estimation method. This method first utilizes a Long Short-Term Memory network to learn the dynamic characteristics of the battery from historical voltage and current data, capturing the long-term dependencies of SOC changes to achieve an initial prediction. Subsequently, using this predicted value as the observation input, an Unscented Kalman Filter based on a second-order RC equivalent circuit model is introduced for optimal state correction, effectively suppressing model uncertainty and measurement noise. Simulation validation under various dynamic conditions, such as constant current discharge and FUDS, shows that compared to single LSTM or UKF algorithms, the proposed fusion method has significant advantages in estimation accuracy, convergence speed, and robustness. Its root mean square error is reduced to 0.0031, and it maintains stable estimation performance under different operating conditions. This study provides an effective data-model fusion solution for high-precision SOC estimation of lithium-ion batteries under complex operating conditions. Full article

Driven by the goal of sustainable energy transitions, the integration of Inverter-Interfaced Distributed Generation (IIDG) has led to a continuous decline in the accuracy of single-phase grounding fault line selection in neutral non-effectively grounded distribution networks. Protection methods based on characteristic signal injection currently struggle to balance the differentiated requirements of fault detection sensitivity and equipment safety in networks with high-penetration IIDG. To address this issue, a high-frequency equivalent circuit model of the IIDG is established. The distribution patterns of the high-frequency characteristic current (HFCC) in distribution networks under high-penetration IIDG are analyzed. Subsequently, an adaptive HFCC injection strategy is proposed, which accounts for IIDG low-voltage ride-through (LVRT) requirements, fault identification sensitivity, and equipment safety constraints. Based on the amplitude and phase differences in the HFCC between faulty and healthy feeders, a fault line selection criterion is established. Consequently, an adaptive injection-based protection method for single-phase grounding fault is developed, considering the impact of high-penetration IIDG. Simulation results demonstrate that the proposed method accurately identifies the faulty feeder under various fault locations, transition resistances, and quantities of integrated IIDG units. The results further confirm the high adaptability and reliability of the method, thereby providing a robust technical foundation for the safe, reliable, and sustainable operation of modern power grids. Full article

Double stator cylindrical linear oscillating generators (DSCLOGs) have been widely used in renewable energy power generation systems due to their higher power density, higher reliability, and low-noise characteristics. However, the detent force of a DSCLOG is an inevitable problem, which causes oscillations in the generator and leads to system instability. Conventionally, auxiliary teeth and skewed pole methods are employed to mitigate detent force, but these approaches often increase the overall machine size and the complexity of the manufacturing process. To solve this issue, an improved DSCLOG with curved teeth (CT-DSCLOG) is proposed to minimize the detent force. First, the structural characteristics and working principle of CT-DSCLOG are introduced. Then, to achieve a rapid and accurate analysis of the magnetic field in the irregular air gap, a 2D magnetic equivalent circuit (MEC) model is established by introducing Schwarz–Christoffel (S-C) mapping. And key structural parameters are identified through variance sensitivity analysis. Subsequently, a multi-objective optimization of the linear generator is performed using the Taguchi method combined with 3D finite element analysis (3D-FEA) to obtain the optimal structural parameters of CT-DSCLOG. Finally, the proposed structure is validated through prototype experiments. The results are provided to validate the effectiveness of the proposed structure. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

High-pressure water injection (HPWI) can rapidly replenish the formation energy of low-permeability reservoirs, but it may trigger multi-scale fractures, leading to premature water breakthrough between injection and production wells. To identify the main causes and regulate the mainstream line (i.e., the preferential flow path with the highest streamline density/flow rate), a two-zone and five-point numerical model was developed. This model couples the static damage zone (dominated by micro-fractures) and the fracture development zone (dominated by macro-fractures). Through sensitivity analysis, the ways in which micro-fracture damage and macro-fracture geometry control the evolution of seepage patterns and the risk of water breakthrough were quantified. The results show that in the representative scenarios of this paper, micro-fracture damage is mainly associated with an increased risk of water breakthrough by forming equivalent weakening zones and enhancing the directional extension trend of main fractures. The scale of macro-fractures has the strongest correlation with the water breakthrough response. When the fracture scale increases to a certain proportion close to the well spacing, the seepage mode changes from “fracture + matrix cooperation” to “main-fracture-dominated short-circuit channel”. Based on this, a design and verification of a combined control scheme of “chemical profile control + cyclic water injection” was proposed and carried out in well groups with high water cut and strong channeling. Simulations show that this combination helps to weaken the flow conductivity of preferential channels and improve the uniformity of the flow field. This paper can provide technical support for the prevention, control, and early warning of water breakthrough and the regulation of main flow lines in the high-pressure water injection development of similar low-permeability reservoirs. Full article

This paper proposes a small size meander-line patch antenna which is designed to have biomedical telemetry applications using the Industrial, Scientific and Medical (ISM) band from 2.40 to 2.48 GHz supported by the equivalent circuit model (ECM). Antenna miniaturization is realized by the effective use of several slot structures placed in the rectangular microstrip patch structure, in order to realize electrical length extension and reduce the physical size. The antenna has overall dimensions of 12 × 22 × 0.787 mm³ and is made on a low-loss Arlon AD 450 (ε_r = 4.50 and tanδ = 0.0035) dielectric substrate, which has the desired stable electrical behavior and, importantly, can be used in implantable environments. Experimental validation is done by implanting the fabricated prototype into a laboratory-manufactured tissue-mimicking phantom, and it showed good agreement with simulated results. The designed antenna has a peak gain of 1.29 dBi in free space and −24.99 dBi at a frequency of 2.45 GHz and a fractional impedance bandwidth of about 250 MHz, which will guarantee reliable operation in the face of diversity and fluctuation in the surrounding environment (biological tissues). Furthermore, specific absorption rate (SAR) analysis is carried out in order to comply with international safety standards with peak SAR values kept within the permissible level of 2 W/kg for 10 g averaging tissue. The results show that the proposed antenna provides a good trade-off between the reduction in size, radiation performance and safety to the patient, making it a good candidate for short-range in-body wireless communication, implantable medical devices, and biomedical monitoring systems. Full article

High concentrations of ionized ammonia (${\mathrm{NH}}_4^{+}$) have been increasingly reported in municipal drinking water systems, posing a severe public health risk as excessive ingestion can lead to life-threatening conditions. Despite its importance, there is a significant lack of sensing technologies designed for continuous-flow monitoring outside laboratory settings, particularly those providing a robust, low-cost methodology suitable for resource-limited environments. To address these challenges, in this work, we report the development of an impedance sensor featuring a 3D-printed housing (3D-IS) for monitoring aqueous ionized ammonia (${\mathrm{NH}}_4^{+}$). The sensing electrodes, composed of zinc oxide and graphite, allow for the detection of concentrations 10 times lower and 60 times higher than current environmental limits. Its innovative, optimized design, analogous to that of industrial pressure gauges, highlights its potential for use in continuous water flow conditions outside the laboratory, such as water treatment plants. The level of ${\mathrm{NH}}_4^{+}$ in water is monitored by changes in impedance magnitude, with optimal performance observed at a frequency of 100 kHz. At this frequency, the impedance magnitude decreased by nearly two orders of magnitude as the ${\mathrm{NH}}_4^{+}$ concentration increased from 0 to 1 μM. Under these optimized conditions, the sensor exhibited a sensitivity of 2 kΩ/log(μM) and a linearity exceeding 90%. Furthermore, we propose an equivalent circuit model that accurately describes the experimental data, explaining the transduction process. We also describe, from an electrical perspective, the phenomenon of adsorption on the sensor’s transducer surface, thereby ensuring the device’s selectivity. The sensor was evaluated using dilutions of a standard ammonium solution for IC in distilled water, as well as with real groundwater samples, obtaining ∼99.7% of correlation with ion chromatography and a limit of detection of 2 μM. Finally, our device can provide information relatively quickly, with the added advantage of stable response under continuous-flow and real conditions, making it an attractive option for integration into a field sensor node. Full article

Accurate modeling of RF capacitors and inductors is critical for predicting circuit behavior and ensuring operational robustness in high-frequency electronic systems. However, SPICE models are often unavailable from manufacturers, and there is still a lack of reliable methodologies for accurate modeling of such passive components over a wide frequency range. This paper presents a systematic and practical equivalent-circuit modeling methodology for capacitors and inductors based on measured impedance data. The proposed approach partitions the entire frequency range into multiple sub-bands and models each using a combination of a core series RLC network and frequency-dependent parallel RC, RL, and RLC sub-networks. This piecewise construction enables the dominant resistive, inductive, and capacitive behaviors to be independently identified and accurately captured in their respective frequency regions, resulting in an accurate broadband equivalent circuit. The resulting models exhibit excellent agreement with target data, demonstrating the reliability of the method. This work provides a practical and systematic procedure for developing accurate broadband models of RF passive components, with validation demonstrated for capacitors up to 6 GHz and inductors up to 20 GHz. Full article

Electric impedance spectrum (EIS) is attracting attention in many areas of science, ranging from electrochemistry and material science to medical diagnosis. Interestingly, theoretical description often stops at material constants and specific physical mechanisms are represented by equivalent circuit elements, which is also motivated by the common use of various bridge methods. This specifically applies to biological samples, which exhibit a rich variety of responses to the electric field. Here, we present a step further from the description that utilizes equivalent circuit elements. We demonstrate how alteration of the mesoscopic structure affects the EIS in a biological sample: a cucumber under thermal treatment that comprises a cooling and warming phase. As the freezing temperature of water is exceeded during the cycle, the cucumber becomes frosted, which leads to unrecoverable changes in the internal structure, with no change of chemical composition. The experimental evidence is complemented by theoretical analysis, based on a novel approach to modeling non-stationary problems, derived from the stationary Poisson–Boltzmann equation. We demonstrate a qualitative agreement between the theoretical and the experimental results, and discuss the procedure for tuning the model. We also demonstrate that, of the temperature variations of the position of the beta dispersion, the one related to the mesoscopic structure, can be used to assess the ionic strength of the material, determine the microscopic diffusion constant, or reflect the changes in mesoscopic structure, depending on experimental protocol. Full article

In this paper, the Distribution of Relaxation Times (DRT) method is introduced for analyzing aging and failure conditions in lithium-ion (Li-ion) batteries, addressing challenges associated with its implementation. While Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are commonly used to monitor battery performance, their interpretation is often complicated by overlapping semicircles in impedance spectra. The DRT technique resolves this issue by deconvolving relaxation times, enabling the separation of individual electrochemical processes and providing a clearer understanding of aging and failure conditions. The peaks of lower frequency components in DRT plots, specifically the charge transfer and diffusion processes, are key indicators of the battery failure point. When these two processes merge, it signals that the battery can no longer function, marking a critical failure point in Li-ion batteries. Identifying failure conditions and aging in Li-ion batteries using DRT offers a more advanced approach compared to ECM, as it delivers greater detail in the electrochemical processes that contribute to performance degradation. The analysis of two kinds of different Lithium-Ion battery cells based on the DRT reveals the specific aging and failure patterns, particularly in later battery life stages. The findings demonstrate the potential of DRT as a real-time indicator to monitor the status and the lifecycle of the battery. Full article

This paper proposes a flexible and polarization-insensitive metasurface (MS) operating at the 5.8 GHz band for electromagnetic energy harvesting. The proposed MS unit features a top-layer dual-ring resonator with a T-shaped gap and a bottom cross-shaped coplanar waveguide (CPW), fabricated on a flexible polyimide substrate. To elucidate the physical mechanism of energy capture, an equivalent circuit model is established based on transmission line theory. Expressions for the total input impedance are derived, revealing the quantitative relationship between the structural parameters and the impedance-matching condition. The simulation results validate this theoretical model and show that the structure achieves an absorption efficiency of 97.5% and a harvesting efficiency (HE) of 86.6% at 5.72 GHz. The conversion efficiency remains above 50% over a wide range of incident angles, and the HE exhibits minimal variation within a polarization angle range of 0–90°. Experimental results indicate that the MS reaches a maximum HE of 73.2%, maintains over 40% efficiency under large-angle incidence, and achieves more than 65% HE across various curved surfaces. With its mechanical flexibility, polarization insensitivity, and simplified manufacturing, this MS harvester provides a reliable and scalable power solution for wireless power transfer applications. Full article

Online identification of HV cable circuits is vital for routine inspection and maintenance, yet existing passive electromagnetic wave injection methods are limited to offline operations. To fill the gap and achieve the online identification of HV cable circuits, an online circuit identification methodology based on sheath current temporal characteristics and deep embedded clustering is proposed. First, an equivalent circuit model of the multi-circuit cross-bonded cable sheath was built to deduce the temporal similarity of sheath currents within the same circuit, establishing the identification criterion. Second, the robustness of the temporal similarity under various operating conditions was verified via simulation based on the Dynamic Time Warping (DTW) distance. Then, a combined model of Temporal Convolutional Network Autoencoder (TCN-AE) and K-medoids was established to transform circuit identification into a temporal clustering problem of sheath currents, realizing circuit determination by synchronously monitoring the time-series sheath current data of multi-circuit HV cross-bonded cables. The method was verified on a full-scale 110 kV cable test platform. The results show that the identification accuracy reached 95.37%, and the proposed method can effectively identify the circuits of cross-bonded cables with high robustness against the domain gap, having significant engineering application value. Full article

Recent advances in energy storage systems and in increasingly efficient, safe, and energy-dense cell chemistries have driven the need for commercial Battery Management System (BMS) architectures with greater control, data acquisition, and communication capabilities, primarily oriented towards customization. This demand introduces a significant change in how electrical systems are modeled and simulated when they integrate active electrochemical elements such as lithium-ion cells. This work presents the development and modeling of a BMS for critical and high-efficiency applications, based on active balancing techniques and incorporating an additional safety stage to respond to failures when charging ${\mathrm{LiFePO}}_4$ cells. The electrochemical model was built using an equivalent RLC circuit and RC pairs to represent the Thevenin response of the cell. For the simulation of active balancers, LTspice was employed, while charging and discharging processes and their effects on state of charge (SOC) and state of health (SOH) were complemented through analysis in MATLAB R2024a.The proposed approach offers an efficient tool for evaluating cell dynamics and validating battery management strategies in demanding scenarios. While the current approach prioritizes the individual modeling of electrical conversion systems, our framework presents an innovative multisystem macromodel, where not only is the electrical behavior simulated but also the control, efficiency, and safety of the system are determined, prioritizing reproducibility through SPICE tools. Full article

Accurate estimation of the state of charge (SOC) is essential for the safe and efficient operation of lithium-ion batteries. Conventional Adaptive Unscented Kalman Filter (AUKF) methods often exhibit limited accuracy, primarily due to the empirical selection of process and measurement noise covariance matrices. To overcome this limitation, this study proposes a QPSO-AUKF algorithm based on a second-order RC equivalent circuit model, which integrates Quantum-behaved Particle Swarm Optimization (QPSO) with online parameter identification. In this approach, the QPSO algorithm optimizes the noise covariance matrices, which are subsequently used within the AUKF framework for SOC estimation. MATLAB R2020a simulations conducted on the Maryland and Wisconsin datasets demonstrate that the QPSO-AUKF reduces the root mean square error (RMSE) by more than 60% compared with the conventional AUKF, indicating a significant improvement in SOC estimation accuracy. Full article