Search Results (150)

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Oxide transistors have attracted significant interest in the field of integrated circuits (ICs). Among various oxide semiconductors, InAlZnO (IAZO) stands out as a promising candidate due to its potential for high mobility and excellent stability. In this work, we fabricate high-performance IAZO transistors with a field-effect mobility of 56.60 cm²/V·s, a subthreshold swing of 82.59 mV/decade, an on-to-off current ratio over 10⁷, and a small threshold voltage shift of 0.09 V and −0.03 V under positive and negative bias stress, respectively. Based on these transistors, Pseudo-Enhancement-Load (PEL) inverters were constructed. An adjustable bias voltage (V_BIAS) was also introduced as an additional control parameter, which allows for flexible control of the trade-off between circuit performance and power consumption. The resulting inverters achieve a balance between static and dynamic performance, exhibiting a voltage gain of 1.83 V/V and a relatively low power consumption of 2.58 × 10⁻⁶ W (V_BIAS = 1.0 V). Our work demonstrates the potential of IAZO transistor-based PEL inverters for high-performance, low-power oxide IC applications. Full article

Previous in vivo studies have clearly demonstrated that the intravenous administration of the carotenoid astaxanthin (AST) suppresses the excitability of rat trigeminal spinal nucleus caudalis (SpVc) neurons. This action is hypothesized to be mediated through the inhibition of both voltage-gated Ca²⁺ (Cav) channels and excitatory glutamate receptor transmission. The objective of this study was to determine whether acute intravenous administration of AST alleviates the hyperexcitability of SpVc wide dynamic range (WDR) neurons in a rat model of inflammation. Neuronal responses to both nociceptive and non-nociceptive mechanical stimulation were evaluated using an in vivo electrophysiological model. One day following inflammation induced by Complete Freund’s Adjuvant (CFA), the mechanical escape threshold was significantly reduced compared to pre-injection baseline values. Subsequently, extracellular single-unit recordings were performed on SpVc WDR neurons in anesthetized, inflamed rats. The neuronal responses to both non-noxious and noxious orofacial mechanical stimuli were then analyzed. Acute intravenous administration of AST at 1 and 5 mM elicited a dose-dependent reduction in the mean firing frequency of SpVc WDR neurons in response to noxious mechanical stimuli. This inhibition peaked within 10 min and was fully reversed after approximately 25 min. Importantly, AST preferentially inhibited the discharge frequency of SpVc WDR neurons in response to noxious stimulation, exhibiting a significantly greater effect than on the response evoked by non-noxious stimulation (41.5 ± 3.0% vs. 20.7 ± 4.2%, p < 0.05). Collectively, these findings demonstrate that acute intravenous administration of AST effectively suppresses noxious synaptic transmission within the SpVc during inflammation. We propose that this suppressive effect is mediated by the inhibition of upregulated Cav channels and glutamate receptors. Consequently, AST is implicated as a promising therapeutic candidate for the management of trigeminal inflammatory pain, given its potential for a favorable safety profile compared to conventional treatments. Full article

Mitochondrial Ca²⁺ signaling is increasingly recognized as a key integrator of synaptic activity, metabolism, and redox balance within the tripartite synapse. At excitatory synapses, Ca²⁺ influx through ionotropic glutamate receptors and voltage-gated channels is sensed and transduced by strategically positioned mitochondria, whose Ca²⁺ uptake and release tune tricarboxylic acid cycle activity, adenosine triphosphate synthesis, and reactive oxygen species (ROS) generation. Through these Ca²⁺-dependent processes, mitochondria are proposed to help set the threshold at which glutamatergic activity supports synaptic plasticity and homeostasis or, instead, drives hyperexcitability and excitotoxic stress. Here, we synthesize how mitochondrial Ca²⁺ dynamics in presynaptic terminals, postsynaptic spines, and perisynaptic astrocytic processes regulate glutamate uptake, recycling, and release, and how subtle impairments in these pathways may prime synapses for failure well before overt energetic collapse. We further examine the reciprocal interplay between Ca²⁺-dependent metabolic adaptations and glutamate homeostasis, the crosstalk between mitochondrial Ca²⁺ and ROS signals, and the distinct vulnerabilities of neuronal and astrocytic mitochondria. Finally, we discuss how disruption of this Ca²⁺-centered mitochondria–glutamatergic axis contributes to synaptic dysfunction and circuit vulnerability in neurodegenerative diseases, with a particular focus on Alzheimer’s disease. Full article

Silicon carbide (SiC) MOSFETs, as one of the representative power electronic devices, have faced reliability challenges due to threshold voltage (V_th) instability under dynamic gate stress. To explore the underlying mechanisms, this work investigates 4H-SiC MOS structures (P-MOS and N-MOS) under AC bias temperature instability (AC BTI) stress, utilizing a laser to generate minority carriers and simulate realistic switching conditions. Through combined capacitance–voltage (C-V) and gate current–voltage (J_g-V_g) characterizations on P-MOS and N-MOS devices before and after degradation at different temperatures, we reveal a critical temperature dependence in defect interactions. At room temperature, degradation is dominated by electron trapping in shallow interface states and near-interface traps (NITs). In contrast, high-temperature stress activates charge exchange with deep-level, slow states. Notably, a positive V_FB shift is consistently observed in both N-MOS and P-MOS devices under AC stress, confirming that electron trapping is the dominant cause of the commonly observed positive V_th shift in SiC MOSFETs. These findings clarify the distinct defect-mediated mechanisms governing dynamic V_th instability in SiC devices, providing fundamental insights for interface engineering and reliability assessment. Full article

We present a mixed-signal front-end ASIC designed for compact Silicon Photomultiplier (SiPM) sensor interfaces, implemented in the AMS 0.35 µm CMOS technology. The chip integrates two independent analog channels, each composed of five custom second-generation current conveyors (CCII⁺), a fast zero-crossing discriminator, and a peak-and-hold stage based on a tailored operational amplifier. The CCII⁺ and discriminator blocks were designed in-house, based on literature designs and adapted to the technology to ensure low input impedance and fast current-mode signal propagation. This architecture enables precise detection of small signals with reduced pile-up, important for time-resolved photon detection. Bias and threshold control are provided by programmable current mirrors and SPI-configurable DACs, including a 10-bit current-mode DAC based on a current-splitting structure with approximately 200 nA resolution. A custom SiPM behavioral model was developed in the Cadence environment to support design and simulation, reproducing realistic pulse shapes and recovery dynamics for timing applications. Circuit-level simulations confirm correct analog functionality and stable operation across the intended dynamic range, with a per-channel consumption of about 5.9 mA at 3.3 V (19.5 mW), reflecting a tradeoff between speed and robustness. The system is compatible with external timing architectures, while internal CCII⁺ stages ensure low-impedance current reception, fast discrimination, and accurate current-to-voltage conversion for peak detection. Full article

In this paper, the impact of negative bias temperature instabilities (NBTIs) on commercial power p-channel Vertical Double-Diffused MOS (VDMOS) transistors from the standpoint of practical applications was analyzed. The effects of NBTI are one of the main reliability concerns for this type of device, so it is necessary to investigate how these effects influence various applications. A series of experiments were carried out including negative bias temperature stressing, infra-red thermographic recording and circuit characterization, with the goal of evaluating the effects of negative bias temperature stressing on the self-heating of samples in load-driving circuits operating with higher currents and circuit performance of a CMOS inverter circuit containing the examined samples. The findings suggest that negative bias temperature stressing-induced threshold voltage shift directly affects increased self-heating in load-driving circuits and that it also directly affects transfer and dynamics characteristics in CMOS inverters. Full article

Optimizing the operating temperature of lithium-ion batteries is critical for safe, reliable, and efficient cell operation. Manufacturers’ recommendations vary in this area, which is primarily determined by the cells’ chemical composition and internal structural characteristics. Most manufacturers define the maximum charging voltage level as the same or close to the same value, but there are significant differences in the lower threshold voltage. Lithium-ion cells exhibit increased internal resistance at lower state-of-charge levels, resulting in elevated heat generation during operation, with intensity proportional to the depth of discharge. However, using a too low voltage threshold causes a significant loss of usable capacity, which reduces the cell’s energy utilization. The present research aims to define and analyze the optimal value of the lower voltage threshold more precisely, considering both thermal development and usable capacity aspects. A further objective is to determine an optimal energy safety margin level that provides a suitable compromise for longer-term storage. Different 18650 and 21700 standard lithium-ion cell types were tested using various load profiles. The results show that the two cell formats have different electro-thermal behaviors. The 21700 cells show a clear increase in thermal efficiency at around 3.1 V. In contrast, the 18650 cells have a heating pattern that depends heavily on the load. This requires selecting a cutoff that adapts to the discharge rate to prevent excessive thermal stress. These findings indicate that a fixed lower threshold voltage for all cells is not ideal. Instead, we need cutoff strategies that are specific to each cell and can change dynamically. The TER-based evaluation introduced in this work provides a practical framework for defining these adaptive limits. It may improve control in battery management systems in real-world applications. Full article

To address the issue of low efficiency in recovering low-frequency vibration energy during vehicle operation, this paper proposes a piezoelectric energy capture harvester based on wheel vibration. The device employs a parallel configuration of dual cantilever beam piezoelectric transducers in its mechanical structure, with additional mass blocks to optimize its resonant characteristics in the low-frequency range. A synchronous switch energy harvesting circuit was designed. By actively synchronizing the switch with the peak output voltage of the piezoelectric element, it effectively circumvents the turn-on voltage threshold limitations of diodes in bridge rectifier circuits, thereby enhancing energy conversion efficiency. A dynamic model of this device was established, and multiphysics simulation analysis was conducted using COMSOL-Multiphysics to investigate the modal characteristics, stress distribution, and output performance of the energy harvester. This revealed the influence of the piezoelectric vibrator’s thickness ratio and the mass block’s weight on its power generation capabilities. Experimental results indicate that under 20 Hz, 12 V sinusoidal excitation, the system achieves an average output power of 3.019 mW with an average open-circuit voltage reaching 16.70 V. Under simulated road test conditions at 70 km/h, the output voltage remained stable at 6.86 V, validating its feasibility in real-world applications. This study presents an efficient and reliable solution for self-powering in-vehicle wireless sensors and low-power electronic devices through mechatronic co-design. Full article

In power systems with a high penetration of renewable energy, integrating battery energy storage systems can enhance frequency regulation capabilities. However, in “islanded” operation mode, the lack of large grid interconnection support may cause significant frequency fluctuations or even instability when the system faces fault disturbances. To ensure the dynamic stability of the grid-forming energy storage system, this paper proposes a virtual synchronous machine (VSM) control parameter tuning and adaptive switching strategy. A control model is developed, which incorporates virtual inertia, damping, droop control, and transient virtual impedance. An optimization model for control parameter tuning is established for two typical disturbances: generator disconnection and three-phase symmetrical short-circuit fault. Additionally, a control parameter adaptive switching mechanism is designed based on voltage threshold and recovery time criteria. The application of this method to a simulation of an islanded power system demonstrates that high damping (e.g., greater than 15 pu) is suitable for generator disconnection disturbances, while a combination of low inertia (0.1 s) and high damping (50 pu) is appropriate for three-phase short-circuit disturbances. The control parameter tuning for three independent and aggregated scenarios successfully achieves effective frequency support. The adaptive switching criteria are set with a voltage threshold of 0.4 pu and a recovery time of 2 s, ensuring that the system frequency recovers within the specified range (48.5–51.5 Hz) within 1 s under short-circuit disturbance. The proposed method shows great potential for improving the operational stability of grid-forming energy storage systems. Full article

High-voltage direct-current (HVDC) systems are essential for large-scale renewable integration and asynchronous interconnection, yet their complex topologies and multi-type faults expose the limits of threshold- and signal-based diagnostics. These methods degrade under noisy, heterogeneous measurements acquired under dynamic operating conditions, resulting in poor adaptability, reduced accuracy, and high latency. To overcome these shortcomings, the synergistic use of knowledge graphs (KGs) and pre-trained models (PTMs) is emerging as a next-generation paradigm. KGs encode equipment parameters, protection logic, and fault propagation paths in an explicit, human-readable structure, while PTMs provide transferable representations that remain effective under label scarcity and data diversity. Coupled within a perception–cognition–decision loop, PTMs first extract latent fault signatures from multi-modal records; KGs then enable interpretable causal inference, yielding both precise localization and transparent explanations. This work systematically reviews the theoretical foundations, fusion strategies, and implementation pipelines of KG-PTM frameworks tailored to HVDC systems, benchmarking them against traditional diagnostic schemes. The paradigm demonstrates superior noise robustness, few-shot generalization, and decision explainability. However, open challenges remain, such as automated, conflict-free knowledge updating; principled integration of electro-magnetic physical constraints; real-time, resource-constrained deployment; and quantifiable trustworthiness. Future research should therefore advance autonomous knowledge engineering, physics-informed pre-training, lightweight model compression, and standardized evaluation platforms to translate KG-PTM prototypes into dependable industrial tools for intelligent HVDC operation and maintenance. Full article

Battery energy storage systems can mitigate power fluctuations and enhance system reliability; however, cell-to-cell inconsistencies and aging in large-capacity battery packs can lead to imbalance. To address the limitations of passive balancing, which suffers from high energy loss and low efficiency, this work proposes a high-current active balancing system based on a single-input multiple-output (SIMO) topology. The system enables energy transfer through a full-bridge converter and transformer, supporting series discharge and selective charging of lithium iron phosphate (LFP) cells. To optimize system performance, a small-signal model was established, and corresponding control strategies were designed: the primary-side inverter employs quasi-open-loop control, while the secondary-side charging modules use a voltage–current dual-loop control. The effectiveness of the model and control strategies was validated via QSPICE simulations. Furthermore, a hybrid active–passive balancing strategy based on a voltage-difference threshold was proposed, allowing for real-time dynamic adjustment of the operating mode according to individual cell voltages. Experimental results on a large-capacity LFP battery demonstrate that the system achieves fast balancing with high accuracy, maintaining cell voltage differences within 30 mV. This provides a practical and effective solution for maintaining cell consistency in electric vehicles and grid-scale energy storage systems. Full article

To mitigate voltage transients caused by power fluctuations in microgrid systems, this study investigates model predictive control and virtual inertia control for the voltage regulation strategy of energy storage unit converters. By drawing an analogy with the virtual synchronous machine equation in AC systems, the virtual capacitor inertia equation is derived for DC systems. Subsequently, model predictive control (MPC) is integrated with virtual inertia (VI) control, leading to the development of an MPC-VI cooperative control method. The reference value for the inner control loop is computed in real time using model prediction, enabling the injection of a counteracting signal opposite to the direction of DC bus voltage fluctuation during disturbances. This approach effectively suppresses rapid voltage variations and enhances system inertia. Furthermore, by incorporating a threshold-based mechanism, the issue of prolonged dynamic response time is mitigated. Simulation and experimental results demonstrate that, compared to conventional control strategies, the proposed MPC-VI method significantly attenuates instantaneous and severe voltage fluctuations, allowing for a more gradual voltage transition during transient events. Additionally, with the implementation of the threshold equation, the system returns to steady state without notable delay, preserving the droop characteristics of the control scheme. Full article

In this paper, an adaptive dynamic event-triggered sliding mode control scheme is proposed for a pneumatic vibration isolation platform. First, an experimental platform is designed and constructed, and a corresponding dynamic model is established, which explicitly accounts for the unknown threshold voltage at the input side. Based on this model, an adaptive sliding mode controller is developed. Then, to suppress unnecessary actuator updates, a dynamic event-triggered mechanism is introduced. Lyapunov-based analysis demonstrates the stability of the closed-loop system and guarantees the exclusion of Zeno behavior. Finally, experimental results on the pneumatic platform verify the effectiveness and superiority of the proposed approach. Full article

Mathematical modeling of unitized regenerative fuel cells (URFCs) faces significant challenges in reconciling parameter conflicts between fuel cell (FC) and electrolysis cell (EC) modes. This study establishes a COMSOL-based multi-physics framework coupling water–gas–heat–electric transport for both operational states. The critical factors associated with the model were identified through a systematic sensitivity analysis of structural and operational parameters, including temperature, exchange current density, conductivity, porosity, and flow rates. FC modes exhibited strong sensitivity to exchange current density (27.8–40.5% performance variation) and conductivity of membrane (10.1–35.6%), while temperature degraded performance (−4.2% to −4.0%). Spatial analysis revealed temperature-induced membrane dehydration and accelerated gas depletion at electrodes, thus explaining the negative correlation. EC modes were dominantly governed by temperature (8.6–9.4%), exchange current density (13.0–16.4%), and conductivity (2.5–13.3%). Channel simulations revealed that elevated temperature contributed to enhanced liquid water fluidity, while high flow rates had a relatively limited effect on mitigating species concentration gradients. Parameter optimization guided by sensitivity thresholds (e.g., porosity > 0.4 in FC GDLs, conductivity > 222 S/m in EC modes) enabled dual-mode calibration. The model achieved <4% error in polarization curve validation under experimental conditions, demonstrating robust prediction of voltage–current dynamics. This work resolves key conflicts of URFC modeling through physics-informed parameterization to provide a foundation for efficient dual-mode system design. Full article