Search Results (323)

Brushless DC motors are widely used for their high power density and efficiency. However, sensorless control remains challenging due to the difficulty of accurate rotor position detection, especially at low speeds. This paper proposes a novel sensorless trapezoidal control method based on Maximum Likelihood Estimation (MLE) for rotor sector detection. Unlike conventional back-EMF zero-crossing techniques, the proposed method uses a statistical algorithm to generate a probability map from prior motor state data, enabling accurate rotor position estimation without sensors. The MLE method operates with a typical computation time of 50–100 $\mathrm{\mu }$s, offering a balanced tradeoff between speed and accuracy. It is significantly faster than Kalman filter-based approaches (200–1000 $\mathrm{\mu }$s) and comparable to observer-based methods (20–80 $\mathrm{\mu }$s), while being more robust than zero-crossing techniques (<5 $\mathrm{\mu }$s). This makes it a practical and cost-effective solution for applications demanding high efficiency and reliability, such as electric mobility systems. Full article

During voltage dips, wind turbines must remain connected to the electrical grid and contribute to voltage stabilization. This study analyzes the impact of voltage dips arising from grid faults on Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECSs). This paper presents a review of the technical regulations for integrating the Algerian electricity grid with the Low Voltage Ride Through (LVRT) system, along with specific requirements for renewable power generation installations. Additionally, the modeling and control strategy of DFIG based WECS has been outlined. Voltage dips can induce excessive currents that threaten the DFIG rotor and may cause harmful peak oscillations in the DC-link voltage, and can lead to turbine speed increase due to the sudden imbalance between the mechanical input torque and the reduced electromagnetic torque. To counter this, a modified vector control and crowbar protection mechanism were integrated. Its role is to mitigate these risks, thereby ensuring the system remains stable and operational through grid faults. The proposed system successfully meets the stringent Algerian LVRT requirements, with voltage dipping to zero for 0.3 s and recovering gradually. Simulations confirm that rotor and stator currents remain within safe limits (peak rotor current at 0.93 $\mathrm{p}\mathrm{u}$, and peak stator current at 1.36 $\mathrm{p}\mathrm{u}$). The DC-link voltage, despite a transient rise due to the continued power conversion from the rotor-side converter during the grid fault, was effectively stabilized and maintained within safe operating margins (with less than 14% overshoot). This stability was achieved as the crowbar ensured power balance by managing active and reactive power. Notably, the turbine rotor speed demonstrated stability, peaking at 1.28 $\mathrm{p}\mathrm{u}$ within mechanical limits. Full article

This paper presents a magnetic-flux-based encoder for BLDC drives that maintains high accuracy under rotor eccentricity and dynamic transients. Conventional Hall-sensor-based angle estimators rely on ideal sinusoidal flux assumptions and degrade in the presence of air-gap distortion or misalignment. To overcome these limitations, a nonlinear autoregressive network with exogenous inputs (NARXNet) is proposed as a temporal neural observer that learns the nonlinear, time-dependent mapping between measured flux densities and the true electrical rotor angle. A physics-informed data augmentation framework combines experimentally measured magnetic flux maps with dynamic simulation to generate diverse training scenarios at low and variable speeds. Validation demonstrates mean angular errors below 2°, 95th-percentile errors under 5°, and negligible drift, with enhanced resilience to eccentric displacement and acceleration transients compared to classical methods. The proposed approach provides a compact, data-driven sensing solution for robust, encoderless electric drive control. Full article

This study rigorously evaluated the integration of energy-harvesting systems within electric vehicles to prolong battery service life. A laboratory-scale system was configured utilizing a scale electric vehicle with a 12.6 V lithium-polymer (Li-Po) battery alongside an automated control platform to precisely estimate the real-time State of Charge (SoC) through monitoring of current, voltage, and temperature of the vehicle battery under three distinct driving conditions: (A) constant velocity at 30 km/h, (B) variable velocities exhibiting a sawtooth profile, and (C) random speed variations. Wind energy was harvested employing Savonius rotor microturbines, with assessments conducted on efficiency losses and drag coefficients to determine the net power yield for each operational profile, which was found to be marginally positive. Considering the energy consumption of electric vehicles based on 2017 U.S. EPA fuel economy data, the maximal recovered energy corresponded to 0.0833% of auxiliary system demand, while the minimal recovery was 0.0398%. These results substantiated the necessity for continued research into sustainable energy management frameworks for electric vehicles. They emphasized the critical importance of optimizing the incorporation of renewable energy technologies to mitigate the environmental ramifications of the transportation sector. Full article

This study presents an experimental framework for mapping the air-gap magnetic flux in electric machines operating under controlled eccentricity and tilt conditions. A six-degree-of-freedom industrial robotic arm positions the rotor, while the stator accommodates a dense single-axis Hall-sensor array. Synchronous data acquisition at 10 kHz captures magnetic-field dynamics during torque-producing excitation. A coordinate-transformation method synthesises virtual rotor poses from a limited set of physical measurements, eliminating the need for exhaustive mechanical scanning. The proposed approach generates pose-resolved RMS and THD maps, together with harmonic amplitude and phase signatures, thereby revealing localised asymmetries and phase-decoherence effects that are not predicted by idealised finite-element models. In a custom PMSM-like prototype, the local RMS value doubles (from 31 mT to 64 mT), while the THD increases by more than 25% across displacement and tilt grids. These findings provide quantitative experimental evidence of misalignment-induced magnetic-field symmetry breaking, supporting model validation and digital-twin calibration for traction, aerospace, and robotic applications. Full article

Hybrid Gas–Magnetic Bearings (HGMBs) are an emerging technology ready to completely change high-speed oil-free rotor support in aerospace electric motors. Because HGMBs combine the stiffness and load capacity of gas bearings with the active control of magnetic bearings, enabling oil-free, contactless rotor support from zero to ultra-high speeds. They offer more load capacity of standalone magnetic bearings while maintaining full levitation across the entire speed range. Dual-mode operation, magnetic at low speeds and gas film at high speeds, minimizes control power and thermal losses, making HGMBs ideal for high-speed aerospace systems such as cryogenic turbopumps, electric propulsion units, and hydrogen compressors. While not universally optimal, HGMBs excel where extreme speed, high load, and stringent efficiency requirements converge. Advances in modeling, control, and manufacturing are expected to accelerate their adoption, marking a shift toward hybrid electromagnetic–aerodynamic rotor support for next-generation aerospace propulsion. This review provides a thorough overview of emerging HGMBs, emphasizing their design principles, performance metrics, application case studies, and comparative advantages over conventional gas or magnetic bearings. We include both a historical perspective and the latest developments, supported by technical data, experimental results, and insights from recent literature. We also present a comparative discussion including future research directions for HGMBs in aerospace electrical machine applications. Full article

Electric induction motors are fundamental to industry, where reliability and continuous operation are critical. Though robust, they are prone to faults, particularly in demanding environments such as highway tunnels. Non-invasive diagnostic techniques are widely used for condition monitoring, yet most studies occur under controlled laboratory conditions, limiting their applicability in real-world scenarios. This research investigates the feasibility of applying Motor Current Signature Analysis (MCSA) for monitoring highway tunnel axial fan motors, aiming to determine its effectiveness for real-time diagnostics in industrial environments. Measurements were performed under actual operating conditions, highlighting practical challenges. Data acquisition was implemented remotely from electrical cabins feeding tunnel services, reducing installation complexity and costs compared to in-tunnel measurements. This approach enabled monitoring of all motors in a tunnel using minimal hardware (a single acquisition system equipped with Rogowski sensors) making the solution cost-effective and suitable for periodic measurements. Frequency domain analysis focused on harmonics associated with rotor bar defects and eccentricity, selected for their slow degradation and diagnostic relevance. The magnitude of these harmonics was tracked over time and compared across motors of the same model. Since most of the time the ventilators are de-energized, the periodic measurements can be seen almost as a real-time monitoring, at least for the faults considered, with much lower costs. Results were validated against maintenance reports, confirming bearing faults with eccentricity in two motors, while suspected rotor porosity remained unverified, as expected at low severity. Findings demonstrate that MCSA can provide operational insights for fault detection in tunnel environments, supporting predictive maintenance strategies. A key outcome of this study was selecting and implementing an effective measurement setup for industrial applications, while preparing the base for future machine learning integration to estimate Remaining Useful Life. Full article

This study proposes a practical and systematic methodology for identifying and characterizing magnetically induced noise in Column Electric Power Steering (CEPS) systems through vehicle-level testing. A coil sensor mounted on the Electric Power Steering (EPS) motor was employed to capture induced voltage signals during steering maneuvers, providing a real-time tachometric reference for order-tracking analysis. In-vehicle acoustic measurements conducted with a binaural headset revealed dominant magnetic harmonics—most notably the 24th order associated with rotor–stator interaction—and their higher-order components. To validate these observations under controlled conditions, complementary experiments were performed in a semi-anechoic chamber. Additionally, structural dynamic properties were evaluated through impact testing to distinguish electromagnetic excitations from mechanical resonances. The proposed methodology demonstrates a cost-effective and accurate approach for assessing the Noise, Vibration, and Harshness (NVH) characteristics of EPS systems, facilitating design optimization and noise mitigation without the need for extensive instrumentation or full-vehicle prototype testing. Full article

The increasing integration of wind power into modern power systems has fostered the demand for reliable frequency regulation strategies, with inertial control emerging as a key solution that utilizes the kinetic energy stored in the wind turbine rotors. Traditional inertial controllers, however, usually depend on fixed gain parameters, which restrict their adaptability under changing grid conditions. This paper introduces a new inertial control strategy that combines a fuzzy logic controller with the Extended Optimized Power Point Tracking (OPPTE) algorithm to improve the frequency response of wind turbines. The fuzzy logic system allows adaptive control by responding dynamically to both frequency deviations and their rate of change, thereby adjusting the turbine’s operating point during emergency events. By shifting the operating point, the system can release more kinetic energy at critical moments, resulting in improved active power injection. The proposed approach was tested through simulation studies in MATLAB/Simulink R2024b using a detailed wind turbine model under various contingency scenarios. The results obtained demonstrate that the proposed strategy surpasses the conventional OPPTE method by significantly improving the maximum value of active power injected into the electrical grid by 6.56% and 9.68% under constant wind and wind series conditions, respectively, as well as reductions in the frequency nadir of 9.6% and 6.4%, and decreases in the frequency change rate of 5% and 4.57% in the exact scenarios. These results demonstrate that combining fuzzy logic with dynamic operating point adjustment provides a practical and effective way to strengthen inertial support and improve grid stability in power systems with high wind power integration. Full article

Demand for brushless alternatives to the series universal motors and induction motors in domestic applications and automotive applications is increasing. Among the available candidates, single-phase flux-switching permanent magnet (SP-FSPM) machines have gained attention due to a simpler magnetic structure and control system. However, their torque density remains limited. Therefore, a SP doubly-fed FSPM (SP-DF-FSPM) machine is developed in this paper which features an additional set of armature windings on the rotor. By effectively utilizing the rotor slot area, the proposed SP-DF-FSPM machine enhances electrical loading and torque density while providing inherent fault-tolerant capability, a critical addition compared with conventional SP-FSPM machines. A comprehensive parameter-sensitivity analysis is conducted for a 10-stator-pole/10-rotor-tooth configuration to optimize key geometric parameters for the maximum torque and reliable self-starting operation. The electromagnetic performance of an optimized design is evaluated and compared against a conventional SP-FSPM machine. The results show that the SP-DF-FSPM machine can achieve a 24.75% higher torque output, improved efficiency, and enhanced power factors under the healthy condition. Moreover, the machine can deliver 63.5% and 36.0% torque when operating with only stator and rotor windings, respectively, demonstrating the fault-tolerant capability. Experimental validation via an SP-DF-FSPM prototype shows close agreement with simulation results. Full article

The honeycomb sealing surface serves as the critical sealing structure between the rotor and stator of an engine, and its sealing performance significantly impacts engine efficiency. To address the challenge of effectively controlling the overcutting depth during the electrolytic grinding of honeycomb sealing surfaces, this study quantitatively determined the actual volumetric equivalent electric charge of the honeycomb grid surface based on Faraday’s law of electrolysis. Nonlinear fitting was employed to establish the decay characteristics of current density and machining efficiency. Machining experiments were designed with voltage and feed speed set as independent variables, and an empirical model coupling the electrolytic proportion with overcutting depth was fitted on the basis of the obtained experimental results. The new parameters were validated, with the model’s predicted values showing an error of approximately 3.5% compared to actual measurements. By selecting the processing parameters using the established empirical prediction model, the overcutting depth of honeycomb seals can be controlled within 0.01 mm while ensuring excellent surface quality, which further meets the high-precision machining requirements for key components such as aviation engine seals. Full article

Aiming at the commutation error in position sensorless control of brushless DC motors (BLDCMs) driven by four-switch three-phase (FSTP) inverters—caused by ignoring capacitor voltage fluctuations—this paper proposes a novel position sensorless control method based on voltage offset compensation. By independently performing PWM modulation on the switches of the non-capacitor-connected phases (Phase a and Phase b), the method suppresses three-phase current distortion. Meanwhile, it calculates the terminal voltages using switch signals and constructs a G(θ) function independent of the motor speed. Based on the voltage compensation amount derived in this paper, the influence of capacitor voltage fluctuations on this function is compensated. According to the relationship between the extreme value jump edges of the G(θ) function (after voltage compensation) and the commutation points, the accurate commutation signals required for motor operation are determined. The proposed strategy eliminates the need for filters, which not only avoids phase delay but also is suitable for motor rotor position estimation over a wider speed range. Experimental results show that compared with the uncompensated method, the average commutation error is reduced from approximately 18° to less than 3° electrical angle. Under different operating conditions, the proposed method can always obtain uniform commutation signals and exhibits strong robustness. Full article

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications. Full article

In-wheel motor (IWM)-driven electric vehicles (EVs) are susceptible to road excitation, which can induce eccentricity between the stator and rotor of the IWM. This eccentricity leads to unbalanced electromagnetic forces (UEFs) and electromechanical coupling (EMC) effects, severely degrading vehicle dynamic performance. To address this issue, this study first established an EMC system model encompassing UEF, IWM drive, and vehicle dynamics. Based on this model, four typical operating conditions—constant speed, acceleration, deceleration, and steering—were designed to thoroughly analyze the influence of EMC effects on vehicle dynamic response characteristics. The analysis results were validated through real-vehicle experiments. The results indicate that the EMC effects caused by motor eccentricity primarily affect the vehicle’s vertical dynamics performance (especially during acceleration and deceleration), leading to increased vertical body acceleration and reduced ride comfort, while having a relatively minor impact on longitudinal and lateral dynamics performance. Additionally, these effects significantly increase the relative eccentricity of the motor under various operating conditions, further degrading motor performance. To mitigate these negative effects, this paper designs an active suspension controller based on distributed model predictive control (DMPC). Simulation and experimental validation demonstrate that the proposed controller effectively improves ride comfort and body posture stability while significantly suppressing the growth of the motor’s relative eccentricity, thereby enhancing motor operational performance. Full article

This paper presents a comprehensive overview of permanent magnet synchronous motors (PMSMs), including their classifications, applications, and vector control strategies. It explores various control techniques, including maximum torque per ampere (MTPA), maximum current (MC), field weakening (FW), maximum torque per voltage (MTPV), sensorless control, and parameter identification, as discussed in this paper. These methods address key challenges in PMSM control, such as improving motor efficiency and accurately estimating rotor position and speed. Additionally, this paper presents the PMSM parameters due to many factors such as electric current, phase angle, saturation, and temperature. The survey findings provide a deeper understanding of PMSMs’ control strategies, aiding in the more efficient and reliable motor studies. Full article