Search Results (604)

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

Background: Transcranial direct current stimulation (tDCS) has emerged as a promising neuromodulatory tool for language rehabilitation in chronic aphasia. However, the effects of bi-hemispheric, multisite stimulation remain largely unexplored, especially in people with chronic and treatment-resistant language impairments. The goal of this study is to look at the effects on behavior and brain activity of an individualized language training program that combines bi-hemispheric multisite anodal tDCS with personalized language training for Albert, a patient with long-standing, treatment-resistant non-fluent aphasia. Methods: Albert, a right-handed retired physician, had transcortical motor aphasia (TCMA) subsequent to a left-hemispheric ischemic stroke occurring more than six years before the operation. Even after years of traditional treatment, his expressive and receptive language deficits remained severe and persistent despite multiple rounds of traditional therapy. He had 15 sessions of bi-hemispheric multisite anodal tDCS aimed at bilateral dorsal language streams, administered simultaneously with language training customized to address his particular phonological and syntactic deficiencies. Psycholinguistic evaluations were performed at baseline, immediately following the intervention, and at 1, 2, 3, and 6 months post-intervention. Resting-state fMRI was conducted at baseline and following the intervention to evaluate alterations in functional connectivity (FC). Results: We noted statistically significant enhancements in auditory sentence comprehension and oral reading, particularly at the 1- and 3-month follow-ups. Neuroimaging showed decreased functional connectivity (FC) in the left inferior frontal and precentral regions (dorsal stream) and in maladaptive right superior temporal regions, alongside increased FC in left superior temporal areas (ventral stream). This pattern suggests that language networks may be reorganizing in a more efficient way. There was no significant improvement in phonological processing, which may indicate reduced connectivity in the left inferior frontal areas. Conclusions: This case underscores the potential of combining individualized, network-targeted language training with bi-hemispheric multisite tDCS to enhance recovery in chronic, treatment-resistant aphasia. The convergence of behavioral gains and neuroplasticity highlights the importance of precision neuromodulation approaches. However, findings are preliminary and warrant further validation through controlled studies to establish broader efficacy and sustainability of outcomes. Full article

Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and $H_{\infty }$ performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the $H_{\infty }$ performance index $\gamma$ were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article

To improve commutation accuracy and effectively suppress torque ripple in brushless DC motors (BLDCMs), this paper presents a novel commutation correction strategy integrated into an enhanced direct torque control (DTC) framework. The proposed method estimates the commutation angle error in real time by analyzing the integral difference in phase currents across adjacent 30° conduction intervals, enabling dynamic and accurate commutation correction. This correction mechanism is seamlessly embedded into a modified DTC algorithm that employs a three-level torque hysteresis comparator and introduces a novel zero-voltage vector selection strategy to minimize torque ripple. Compared with conventional DTC approaches employing dual-loop control and standard zero vectors, the proposed method achieves up to a 58% reduction in torque ripple along with improved commutation precision, as demonstrated through both simulation and experimental validation. These results confirm the method’s effectiveness and its potential for application in high-performance BLDCMs drive systems. Full article

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R₁, R₉, R₁₂), capacitor (C₈), and motor parameters, including armature resistance (R_a), inertia (J), and friction coefficient (B). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education. Full article

This article proposes a nonlinear control strategy to address the voltage instability issue caused by the boost converter with an uncertain constant power load (CPL). This strategy combines a passivity-based controller (PBC) with a virtual DC motor controller (VDCM). Initially, a PBC is designed for the boost converter, which enhances the robustness of the converter with CPL perturbations in the DC bus voltage. To overcome the limitations of PBC, including steady-state errors resulting from variations in load or input voltage, the VDCM is incorporated, simulating the characteristics of a DC motor. This addition improves the system’s inertia and damping, making it more stable and significantly enhancing its dynamic performance. The efficacy and stability analysis of the proposed control strategy is validated through both simulation and experimentation. Full article

DC link power supply systems that integrate power electronic converters are increasingly being adopted. In particular, emerging “source–load” systems, in which the DC link interfaces with converters, have attracted increasing research interest due to concerns about power quality and system stability. This paper addresses mid- and low-frequency oscillation issues in DC link voltage supplied induction motor drives (IMDs). It begins by constructing a multiple-input multiple-output (MIMO) state-space model of the induction motor. For the first time, the dq-axis control system is represented as an equivalent admittance model that forms two single-input single-output (SISO) loops. The PI controller and induction motor are integrated into the inverter’s input impedance model; Furthermore, the effectiveness and accuracy of the derived impedance model are experimentally validated under various operating conditions of the induction motor using a custom-built test platform. The experimental results offer a practical reference for system enhancement and stability evaluation. Full article

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response. Full article

Pulse-width modulation (PWM) and pulse-density modulation (PDM) are widely used in applications where electrical energy is delivered in a pulsed manner. Typical examples include LED (light-emitting diode) control, DC motor control, switched-mode power supplies (SMPS), and electric heating control. However, the pulsed operation of power switches is often associated with significant electromagnetic interference (EMI). As an alternative, stochastic pulse-density modulation (SPDM), also referred to as stochastic signal density modulation (SSDM), can be considered. This technique distributes the energy of generated harmonics over a broader frequency spectrum, thereby reducing the amplitude of individual frequency components. As a result, unwanted frequencies become easier to filter out, mitigating EMI more effectively. Full article

Background: Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique for migraine prevention. This study evaluates the efficacy of tDCS compared to sham in preventing episodic migraine in adults. Methods: PubMed and Embase databases were searched until May 2025 to identify randomized controlled trials comparing tDCS with sham for the prevention of episodic migraine in adults. Risk of bias in the included trials was assessed using the Cochrane Risk of Bias Tool version 2. A random effect meta-analysis was conducted to evaluate the effects of cathodal and anodal tDCS on migraine frequency (days per month and attacks per month). Results: The meta-analysis included six trials with 172 participants (mean age 34 years, 82% females). Both cathodal (three studies, over the occipital area) and anodal (three studies, over the occipital or primary motor area) tDCS reduced the mean number of monthly migraine days and migraine attacks compared to sham. After pooling the outcomes and excluding two studies at high risk of bias, anodal tDCS over the occipital or primary motor area (standardized difference in means = −0.7, 95% CI: −1.7, 0.2, p = 0.124) and cathodal tDCS over the occipital area (standardized difference in means = −0.7, 95% CI: −1.1, −0.3, p = 0.000) reduced headache frequency compared to sham. However, the reduction with anodal tDCS was not statistically significant. Summary: tDCS may be effective in preventing episodic migraine. However, the evidence is limited by the small number of heterogeneous trials, with variation in electrode placement and stimulation intervals. Full article

Brushless DC (BLDC) motors are widely used in applications such as Electric Vehicles (EVs) due to their high efficiency, low maintenance, and favorable torque-to-mass ratio. However, one major challenge in BLDC motors is the presence of current harmonics, which can lead to increased noise, vibration, and reduced efficiency, particularly at low speeds or light loads. These harmonics primarily arise from abrupt current transitions during phase commutation. To address this, thispaper presents an innovative approach that combines the Proposed Sigmoid Trapezoidal Current Model with hysteresis control to reduce current harmonics. The model facilitates smooth current changes by applying a sigmoid function, replacing sharp transitions with gradual ones, thus significantly minimizing harmonic distortion. Additionally, hysteresis PWM control enhances the system by precisely regulating the current and dynamically adjusting the switching frequency to maintain the current within a defined range. Simulation results confirm the effectiveness of this method, showing substantial reductions in current harmonics, speed ripple, and torque ripple. Specifically, the proposed method reduces torque ripple by 81% compared to traditional Electronic Commutation Control and improves torque ripple by 30% compared to the conventional method. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article