Search Results (1,004)

Extended Kalman Filters (EKFs) are widely employed for attitude estimation using Magnetic and Inertial Measurement Units (MIMUs) in battery-powered sensing systems. In such applications, energy availability influences system operation, yet battery state information is commonly treated by external supervisory mechanisms rather than being integrated into the estimation process. This work presents an EKF-based formulation in which the battery State of Charge (SOC) is explicitly included as a state variable, allowing joint estimation of attitude and energy state within a single filtering framework. SOC dynamics are modeled using a low-complexity estimator based on terminal voltage and current measurements, while attitude estimation is performed using a Simplified Extended Kalman Filter (SEKF) tailored for embedded MIMU-based applications. The proposed approach was evaluated through numerical simulations under constant and time-varying load profiles representative of low-power electronic devices. The results indicate that the inclusion of SOC estimation does not affect the attitude estimation performance of the original SEKF, while SOC estimation errors remain below 8% for the evaluated load conditions with power consumption of approximately 0.1 W, consistent with wearable and small autonomous electronic platforms. By incorporating energy state estimation directly into the filtering structure, rather than treating it as an external supervisory task, the proposed formulation offers a unified estimation approach suitable for embedded MIMU-based systems with limited computational and energy resources. Full article

Most electric vehicles use 400 V batteries, while some companies are moving to 800 V to reduce current in electric drives. More cars are expected to adopt 800 V at the DC terminals of the batteries, but 400 V will remain common for the duration of this transition, so future off-board chargers must support a wide voltage output range. Silicon carbide switches are used to keep the power–electronics interface compact and scalable. The AC/DC stage of a modular silicon carbide-based interface is designed using a T-type active front end and a dual active bridge. The T-type front end is optimized with a genetic algorithm. The resulting model is used to tune the inner current and outer voltage controllers. Bode analysis shows an inner current loop bandwidth of 4.25 kHz with a phase margin of ${53}^{\circ }$ and a gain margin of 30 dB. The outer voltage loop reaches 50 Hz with a phase margin of ${108}^{\circ }$ and a gain margin of 33 dB. The controller is implemented on a dSPACE MicroLabBox. Tests show peak efficiency of 98.5% in G2V mode and 98.3% V2G mode. THD stays under 5% above 4 kW and reaches 3% at peak power. Full article

Hybrid supercapacitors possess high power and energy density, while the cascaded H-bridge converter features rapid response capability. Integrating these two components leads to an energy storage system capable of swiftly responding to power demands, effectively mitigating voltage and frequency instability in weak-grid renewable energy stations. Based on this system, in this paper, a novel automatic frequency–voltage regulation strategy is proposed. First, a fast fault severity detection method is proposed. It evaluates the system’s fault condition by monitoring the voltage response and generates auxiliary signals to enable subsequent rapid compensation of voltage and frequency. Subsequently, fast automatic voltage and frequency regulation strategies are developed. These strategies leverage real-time fault assessment to deliver immediate power support to weak-grid renewable stations following a disturbance, thereby effectively stabilizing the terminal voltage magnitude and system frequency. The effectiveness of the proposed method is validated through simulations. A grid-connected model of a weak-grid renewable energy station is established in MATLAB (2023b)/Simulink. Tests under various fault scenarios with different short-circuit ratios and voltage sag depths demonstrate that the proposed strategy can rapidly stabilize both voltage and frequency after large disturbances. Full article

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

GNE myopathy is a rare genetic neuromuscular disorder caused by mutations in the GNE gene. The respective gene product, UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase (GNE), is a bifunctional enzyme that initiates endogenous sialic acid biosynthesis. Sialic acids are important building blocks for the glycosylation machinery of cells and are typically found at the terminal ends of glycoprotein N- and O-glycans. The exact pathomechanism of GNE myopathy remains elusive, and a better understanding of the disease is urgently needed for the development of therapeutic strategies. The purpose of this study was to examine the effects of hyposialylation on glycan structures and subsequent downstream effects in the C2C12 Gne knockout cell model. No overall remodeling of N-glycans was observed in the absence of Gne, but differences in glycosaminoglycan expression and O-GlcNAcylation were detected. Expression analysis of myopathogenes revealed concomitant down-regulation of muscle-specific genes. Among the top candidates were the sodium channel protein type 4 subunit α (Scn4a), voltage-dependent L-type calcium channel subunit α-1s (Cacna1s), ryanodine receptor 1 (Ryr1), and glycogen phosphorylase (Pygm), which are associated with excitation-contraction coupling and energy metabolism. The results suggest that remodeling of the glycome could have detrimental effects on intracellular signaling, excitability of skeletal muscle tissue, and glucose metabolism. 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

In this study, a Superb Fairy-wren Optimization Algorithm (SFOA)-based proportional–integral–derivative (PID) controller is proposed for the first time in the literature to improve transient voltage stability performance of automatic voltage regulator (AVR) systems. The proposed approach aims to optimally tune the PID controller gain parameters $K_P$, $K_I$ and $K_D$, which are used for voltage regulation in AVR systems. As a result of the optimization performed using SFOA, PID gain parameters are obtained as $K_P$ = 0.5914, $K_I$ = 0.4078 and $K_D$ = 0.1954. According to the transient voltage response analysis results, the SFOA-based PID controller showed superior performance, with a maximum overshoot of 0.02307, a rise time of 0.33 s, peak time of 0.636 s, fastest stabilization with settling time of 0.514 s within the ±2% tolerance band and steady-state error of 0.0012. Its performance was superior to several state-of-the-art, optimization-based methods reported in the literature. According to commonly used objective functions in AVR systems, integral of time absolute error (ITAE) and Zwe-Lee Gaing’s objective functions, the best results were obtained with values of 0.0489 and 0.0826, respectively. The results show that a SFOA-based PID controller can be an alternative and effective control approach for AVR systems with strong potential for optimization-based control applications in electric power systems. Full article

Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article

Surface charge accumulation and trap distribution are the core factors affecting the surface flashover characteristics of insulating materials. Considering the low-temperature gradient environment of superconducting energy pipeline terminations, this paper systematically studies the surface charge dynamic characteristics and trap distribution law of epoxy glass fiber (GFRP) by using the isothermal surface potential decay (ISPD) method combined with finite element simulation. A temperature-controlled ISPD test platform of −30~20 °C (193~293 K) was built to measure the surface potential decay curves at different temperatures and calculate the trap energy level and density; a charge migration model considering temperature gradient was established to analyze the influence of trapped charges on surface potential and electric field distribution. The results show that low temperature significantly reduces the surface potential decay rate (the residual potential after 5000 s is 92.91% of the initial value at 193 K, and only 3.51% at 293 K); the traps of GFRP at 193 K are dominated by deep traps (central energy level 0.68 eV, density 1.63 × 10²⁰ m⁻³·eV), while there is a bimodal distribution of shallow traps (0.92 eV) and deep traps (0.98 eV) at 293 K; under temperature gradient, the accumulation of deep trap charges in the low-temperature region leads to a surface electric field distortion rate of 12.60, which is the key microscopic mechanism for the decrease of flashover voltage. Full article

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article

The development of high-performance DC circuit breakers (DCCBs) and rapid fault detection schemes is a crucial and challenging part of advancing Modular Multilevel Converter (MMC) HVDC grids. This paper introduces a new current-limiting DCCB that uses the differential discharge times of shunt capacitors to generate artificial current zero-crossings, thus facilitating arc quenching. This mechanism significantly reduces the effect of fault currents on the MMC. The shunt capacitors and arresters in the proposed breaker also offer voltage support during faults, effectively stopping transient traveling waves from spreading to nearby non-fault lines. This feature creates an effective line protection boundary in multi-terminal HVDC systems. Additionally, a fast fault detection scheme with primary and backup protection is proposed. A four-terminal MMC-HVDC (±500 kV) simulation model is built in PSCAD/EMTDC to validate the scheme. The results demonstrate the excellent fault detection performance of the proposed method. The voltage and current behavior during the interruption process of the new DCCB is also analyzed and compared with that of a hybrid DCCB. Full article

Induction motors (IMs) are widely used in variable-speed electric drive systems, where the motor is supplied by a voltage source inverter (VSI). Thus, PWM inverter–IM combination presents several issues that can degrade system performance, particularly overvoltage phenomena when long cables are used. In inverter-fed drive systems, the physical separation between the converter and the motor often requires long motor cables, which can significantly affect voltage stress. As the inverter’s output pulses propagate through the cable, voltage reflections and high-frequency oscillations occur at the motor terminals. We theoretically and experimentally investigate the effect of three PWM methods, namely Space Vector (SVPWM), Selective Harmonic Elimination PWM (SHEPWM), and Random PWM (RPWM) strategies, on overvoltage at the terminals of an induction motor fed by a PWM inverter through a long cable. The simulation results exhibit the validity and efficiency of SVPWM control to reduce overvoltage for different cable lengths. In addition, in order to reduce and eliminate all overvoltage peaks, three filters are proposed and evaluated: an RC filter, an RLC filter, and a compensator. The proposed PWM strategies are assessed using equivalent experimental results obtained on an induction motor fed by a two-level VSI. The experimental tests demonstrate also the efficiency of the SVPWM compared to other strategies. Full article

With the irregular integration of small-capacity distributed generators (DG) and single-phase loads, rural low-voltage distribution transformers are faced with issues such as three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions, impacting the safe and stable operation of the system. To address this issue, a coordinated control strategy based on direct power control (DPC) for low-voltage substation series-parallel coordination is proposed. A flexible interconnection topology for multi-substation series-parallel coordination is designed to achieve coordinated optimization of alternating current–direct current (AC-DC) power quality. Addressing the three-phase imbalance, light-heavy loading, and feeder terminal voltage excursions in rural low-voltage distribution transformers, a series-parallel coordinated optimization control strategy is proposed. This strategy incorporates a DC bus voltage control strategy based on sequence-separated power compensation and a closed-loop control strategy based on phase-separated power compensation, effectively addressing three-phase imbalances and load balancing in each power distribution areas. Furthermore, a series-connected phase compensation control strategy based on DPC is proposed, efficiently mitigating feeder terminal voltage excursions. A corresponding circuit model is established using Matlab/Simulink, and simulation results validate the effectiveness of the proposed strategy. Full article

Triboelectric nanogenerators (TENGs) offer intrinsically high open-circuit voltages in the kilovolt range; however, conventional diode rectifier interfaces clamp the voltage prematurely, restricting access to the high-energy portion of the mechanical cycle and preventing delivery-centric control. This work develops a unified physical basis for contact–separation (CS) TENGs by confirming the consistency of the canonical $V_{oc}$–$C_s$ relation with a dual-capacitor energy model and analytically establishing that both terminal voltage and storable electrostatic energy peak near maximum plate separation. Leveraging this insight, a self-powered gas-discharge-tube (GDT) rectifier bridge is devised to replace two diodes and autonomously trigger conduction exclusively in the high-voltage window without auxiliary bias. An inductive buffer regulates the current slew rate and reduces $I^{2}R$ loss, while the proposed topology realizes two decoupled power rails from a single CS-TENG, enabling simultaneous sensing/processing and actuation. A low-power microcontroller is powered from one rail through an energy-harvesting module and executes an operator-based nonlinear controller to regulate the actuator-side rail via a MOSFET–resistor path. Experimental results demonstrate earlier and higher-efficiency energy transfer compared with a diode-bridge baseline, robust dual-rail decoupling under dynamic loading, and accurate closed-loop voltage tracking with negligible computational and energy overhead. These findings confirm the practicality of the proposed self-powered architecture and highlight the feasibility of integrating operator-theoretic control into TENG-driven rectifier interfaces, advancing delivery-oriented power extraction from high-voltage TENG sources. Full article

Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict battery behavior and estimate critical states such as state of charge (SOC) and state of health (SOH). In this study, a hybrid deep learning approach has been introduced for FOM PI, representing the first application of deep learning in this domain. A simulation platform was developed to generate datasets using Sobol and Monte Carlo sampling methods. Five deep learning models were constructed: long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1DCNN), and hybrid models combining 1DCNN with LSTM and GRU. Hyperparameters were optimized using Optuna, and enhancements such as Huber loss for robustness to outliers, stochastic weight averaging, and exponential moving average for training stability were incorporated. The primary contribution lies in the hybrid architectures, which integrate convolutional feature extraction with recurrent temporal modeling, outperforming standalone models. On a test set of 1000 samples, the improved 1DCNN + GRU model achieved an overall root mean square error (RMSE) of 0.2223 and a mean absolute percentage error (MAPE) of 0.27%, representing nearly a 50% reduction in RMSE compared to its baseline. This performance surpasses that of the improved LSTM model, which yielded a MAPE of 9.50%, as evidenced by tighter scatter plot alignments along the diagonal and reduced error dispersion in box plots. Terminal voltage prediction was validated with an average RMSE of 0.002059 and mean absolute error (MAE) of 0.001387, demonstrating high-fidelity dynamic reconstruction. By advancing data-driven PI, this framework is well-positioned to enable real-time integration into battery management systems. Full article