Permanent magnet motors, such as Brushless DC (BLDC) motors, are widely adopted in today’s industry for their high power density, torque-to-weight ratio, and uncomplicated design. Furthermore, electrical motor drive systems are increasingly adopting (position) sensorless control methods. Sensorless techniques have several advantages over sensor-based methods, including enhanced reliability, prolonged motor lifespans, simplified inverter-motor connections, and lower costs. Several sensorless techniques have been proposed over the past few decades, such as methods based on terminal voltage measurement, third harmonic back electromotive force (back-EMF) signals, and estimation methods. Research in recent years has focused on improving conventional sensorless methods and developing new ones, including those based on flux linkage functions and neural networks. These advancements address challenges associated with high-speed BLDC drives where the phase lag of the LPF is significant, tackle issues in very low-speed applications where back-EMF signal amplitudes are low, improve position estimation speed to enable one-cycle estimation, enhance control system accuracy for BLDC motors with nonideal back-EMFs, and consider asymmetric back-EMF signals. This paper examines the benefits, limitations, and challenges of sensorless drives for BLDC motors, alongside innovative solutions for nonideal and asymmetric back-EMF issues, with an eye toward future developments.

In various applications, the reliable and efficient detection of faults in electric machines is crucial, particularly in environments with high noise levels. To this end, the current study introduces an effective fault detection model utilizing the differential of Short-Time Fourier Transform (STFT) and a channel-wise regulated Convolutional Neural Network (CNN). The novel use of the differential of STFT is presented to enhance the diagnostic model's performance in noisy conditions compared with the conventional STFT. According to the inherent time-frequency domain information within the differential of STFT, a regulated CNN-based model is proposed to integrate spatio-temporal information into the feature map, thereby enhancing accuracy and reducing the computational demand. The method is evaluated on three datasets: the widely used Case Western Reserve University (CWRU) benchmark featuring bearing fault and vibration measurements, a dataset involving Permanent Magnet Synchronous Motor (PMSM) data with varying levels of Inter-Turn Short-Circuit (ITSC) fault and current measurements, and a dataset consisting of a mixture of mechanical and electrical faults. Comparative analysis highlights the superior performance of the proposed model over existing robust methods in the literature under both normal and noisy conditions. Code: https://github.com/artaalikhani/ChRegNet

This paper reviews the application of Hall position sensors in control and position estimation of permanent magnet synchronous motors (PMSMs). Accurate rotor position and motor speed data are essential for the high-efficiency control of PMSMs in modern industry. Rotor position and motor speed can be measured by mechanical position sensors, which are costly and less reliable, or by rotor position observers, which are sensitive to system models and changes in motor parameters. This paper examines the benefits, limitations, challenges, and uses of low-resolution Hall position sensors in PMSM drives, presenting them as a cost-effective solution for achieving a balance between performance and expense. In addition, the paper discusses recent solutions to issues related to misplaced Hall position sensors and fault-tolerant control algorithms, and gives an outlook to future developments in this area.

This paper presents a new circuit topology for High Voltage Gain Active Switched boost quasi Z-source Inverter (HGAS-qZSI) and a pulse width modulation (PWM) technique. The higher boosting capability with a lower number of elements, the continuous source current, the less voltage stress across the devices, and having the common ground are the main advantages of the proposed structure. The voltage gain of the proposed inverter is twice that of the conventional capacitor-assisted qZSI (sCA-qZSI). Other benefits of the proposed inverter over the sCA-qZSI are the reduction of Z-source passive components and shoot-through current. Compared to the conventional active switched inductor boost qZSI (cASLB-qZSI), the proposed inverter has higher output voltage and also lower shoot-through current, while it has fewer components (one fewer inductor, two fewer diodes, but one more capacitor). The boost factor of the proposed inverter is more than twice that of the classic ones. Besides, a high gain PWM technique is proposed which can be applied to any types of Z-source inverters (ZSIs). Both the simulation and experimental results of the proposed inverter under the proposed PWM technique are examined to validate their operation.

Reducing manufacturing costs and increasing reliability are two major issues for the automotive industry. The development of sensorless control methods and estimation techniques for synchronous machines are ways to reduce the impact of these constraints. In this paper, a control method is applied to a Wound Rotor Synchronous Machine (WRSM), often used in HEV applications, to estimate stator currents and control the machine without using any current sensor in the stator side. In this estimator, instead of the electric state model based on the currents, a flux based model is used, which increases the precision of the estimator, improve the dynamics of stator currents, and has a better behavior in case of magnetic saturation. Using this model and applying a Luenberger observer, the stator currents are estimated and then used in the current control loop. The performances of the proposed method are studied in a simulation part. Experimental tests are also performed to verify the effectiveness of the proposed control method.