This paper focuses on the efficiency analysis and optimization of the Yoke-less Axial-flux Synchronous Motor (YASA), a structured axial flux permanent magnet motor. Firstly, the advantages of the YASA motor, as a new motor topology, are introduced, including high power density, high torque density, efficiency, and a slim design. Secondly, using the YASA motor as an example, a three-dimensional electromagnetic field calculation model is established, and the effects of the slot filling factor, tooth shoe thickness, and tooth shoe width coefficient on motor efficiency are analyzed. By optimizing these design variables, the iron core loss and eddy current loss of the motor are systematically reduced using response surface methodology and genetic algorithms, significantly improving the motor's efficiency. Finally, finite element analysis is used to verify the feasibility of the optimized scheme on a 10-pole, 12-slot motor. The results show that the iron core loss and eddy current loss of the motor are reduced by 36.5% and 37.8%, respectively.

For the full-speed sensorless control of permanent magnet synchronous motors (PMSM) used in robotic joints, conventional methods adopt a combined strategy of zero-low-speed and medium-high-speed control methods. This requires switching between methods during motor operation, which reduces the reliability and stability, making it unable to be used in robotic joint motors. In this paper, a novel full-speed range sensorless control method is proposed. The method consists of two ranges: the zero-speed and the operation range. In the zero-speed range, a high-frequency square wave injection (HFSI) method with only 20 square-wave pulses (SWPs) is employed, resulting in a short injection duration, which ensures low noise. Leveraging the bidirectional convergence property of the linear time-invariant enhanced phase-locked loop (LTI-EPLL), the initial position and NS polarity can be determined without additional signal injection, ensuring smooth startup and rotor standstill. In the operation range, based on the eletrical properties of the PMSM, a sliding-mode speed observer (SMSO) is constructed to directly estimate the speed, and the position is obtained via integration. Thus, full-speed range operation is achieved without the need to switch between methods during operation range. Finally, the feasibility of this method is validated in MATLAB/Simulink.

Insulated Gate Bipolar Transistors (IGBTs) play a vital role in power electronics, producing large volumes of time-series data critical for fault detection and system health monitoring. The sheer data size challenges efficient storage and transmission, especially in IoT and edge computing scenarios. Conventional compression techniques, like wavelet transforms and PCA, often struggle to retain the complex, non-linear patterns in IGBT signals, leading to reduced reconstruction quality and impaired fault diagnosis. To overcome these issues, we introduce a novel Multi-Scale Residual Attention Generative Adversarial Network (MRA-GAN) tailored for IGBT data compression and reconstruction. The model features a generator with multi-scale convolutions, residual connections, and a spatiotemporal attention mechanism to effectively capture diverse signal characteristics. A discriminator ensures the reconstructed data closely mimics real IGBT signals through adversarial training. By balancing reconstruction accuracy and data realism, MRA-GAN achieves high compression ratios while preserving fault-related features. Evaluations show it outperforms traditional methods, supporting precise fault detection and enabling efficient data handling for real-time industrial monitoring. This approach significantly enhances data processing for IGBT applications, offering a scalable solution for power electronics diagnostics and resource-constrained environments.

Multimotor drives have become increasingly important in modern industrial applications due to their ability to provide superior performance, efficiency, and flexibility compared to single-motor systems. Hence, this article presents an overview of recent advancements in multimotor drives, focusing on three main areas: structural diversity, advanced control, and emerging challenges and solutions. First, the various structural configurations of multimotor drives are summarized, which include parallel, cascaded, and hybrid configurations. The features as well as component motors and converters of each configuration are discussed, along with the selection rules of a particular configuration for a given application. Second, from the perspective of different performance requirements, the advanced control technologies used for multimotor drives are discussed. Then, this article highlights the technical challenges associated with multimotor drives, including coordination control, mutual interference, communication, interdependent fault diagnosis, and power quality. Meanwhile, viable solutions to these challenges are summarized. Finally, a discussion of the future directions and opportunities for further research and development in the field of multimotor drives is presented. Through this article, scholars and engineers can gain a comprehensive understanding of current and future developments in multimotor drives, contributing to continued research in this field and facilitating successful integration into various applications.

Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

In the area of electric submersible pumps (ESPs), permanent magnet motor (PMM) drives are drawing increasing attention due to the advantages of high energy efficiency and high-power density. However, there are few studies focusing on the specific issues of the ESP PMM drives. To solve the issue, this paper emphasizes the voltage drop and position estimation inaccuracy issues of the system. First, the voltage drops caused by the LC filter, transformer, and long cables are analyzed, providing guidelines for designing the appropriate PMM drives. Second, the impacts of system structure on position and speed estimation accuracy are explained at length, posing necessity of developing improved sensorless control strategies. Finally, simulation is conducted on a PMM drive to verify the effectiveness of the analytical results.

In the application of coal mining belt conveyors, dual-motor drives based on permanent magnet motors (PMM) are gaining increasing attention now. To achieve high-performance coordination control of the two motors, this paper proposes a finite control set model predictive speed control (FCS-MPSC) method to improve the dynamics and speed tracking performance of the motors. First, the features of the dual-motor drives used in conveyor belts are analyzed. On this basis, the requirements of the control strategies are illustrated. Second, a master-slave control strategy is developed after treating the PMMs at the tail end and head end as the master motor and slave motor, respectively. Third, the FCS-MPSC method is developed for both master and slave motors by using new predicting model. In this process, the issue that the speed property is not directly related to the manipulated variables are tackled. Moreover, in order to further improve the dynamics of the slave motor, a speed reference compensation strategy is proposed. Finally, the proposed FCS-MPSC method is validated through comparative simulation results.

This paper proposes a novel concept to eliminate the digital delay effect on rotor position for the traditional two-step finite control set model predictive current control (FCS-MPCC) method used in the PMSMs. Specifically, that the digital delay affects the position, and further the prediction accuracy is discussed firstly, posing the necessity of compensating the position used for control. Then, a speed-prediction-based linear compensation method which takes the speed shift trend into account is proposed to eliminate the digital delay impact on position, which is conducive to the control performance. Finally, simulation is conducted on a three-phase PMSM to validate the proposed strategy in comparison with the traditional two-step FCS-MPCC.

This paper presents a load change assessment-based feedforward compensation method for finite control set model predictive current control (FCS-MPCC) in permanent magnet synchronous motors (PMSMs). The objective is to address the adverse effects of load disturbances on FCS-MPCC, which can lead to deteriorated control performance and system instability. To mitigate these effects, a novel feedforward compensation mechanism is proposed by integrating a load change assessment mechanism within the FCS-MPCC framework. The mechanism enables real-time estimation of load changes by accurately capturing their rate and direction. A sliding mode torque observer (SMTO) is developed to ensure accurate load estimation, characterized by fast response and strong robustness. The stability of the SMTO is analyzed using a Lyapunov function. Furthermore, a technique is proposed to generate feedforward compensation values based on the load change assessment, specifically applied to the q-axis reference current. Comparative simulation results verify the effectiveness of the proposed strategies.

The diagnosis and fault-tolerant control of open-phase faults in permanent magnet synchronous motor (PMSM) drive significantly rely on models describing post-fault behaviors of current and voltage. This paper aims to propose an improved current prediction model that can accurately estimate the post-fault current dynamics under the single open-phase fault. Firstly, in each switching state, the post-fault phase voltages are derived by considering the floating neutral voltage incurred by the back electromagnetic force (back-EMF). Furthermore, by incorporating the derived phase voltages as the activation sources in each switching cycle, the post-fault current prediction model is established. Moreover, the changes of inductances under the open-phase fault are taken into account by establishing the model in the independent phase model. Finally, experimental results validate the derived post-fault phase voltages and the post-fault current prediction model.

Interior permanent magnet synchronous machines (IPMSM) often work under flux weakening state in electrical vehicles for higher speed. But unavoidable faults might occur during that process, after which the power transistors are switched off immediately and the battery packs are charged through the three-phase uncontrolled rectifier composed of six flywheel diodes. That is also renowned as uncontrolled generation (UCG) operation which needs to be well understood to avoid damage to either inverter or battery. In order to achieve more accurate analytical results, this paper proposes an improved steady-state model of the drive system for UCG analysis. On the basis of the model, the parameter dependencies are detailed. The voltage and current characteristics of UCG and the effectiveness of the proposed steady-state model are verified by experimental results.

This paper directly uses the machine windings to discharge the capacitor voltage in the PMSM drive systems when an emergency happens to electric vehicles. The winding-based method needs to control the d, q-axis current in the motor and it requires accurate position information during the whole discharge process. Whereas, the position sensor installed in the motor is likely to fail due to the violent vibration and impact in the emergency. Considering the position sensor faults, this paper proposes a more reliable current control algorithm based on a sliding mode position estimator to bleed the capacitor voltage. The proposed discharge strategy treats the machine as a generator so as to regulate the DC-bus voltage, and takes advantage of the maximum discharge capacity of the drive system and manages to maintain the discharge time within 2.7 s for the studied system. Simulation and experimental results are presented to verify the effectiveness of the proposed algorithm.

When an emergency happens to electric vehicles, the voltage of the dc-bus capacitor, which is an important part of the permanent magnet synchronous machine (PMSM) drive system, requires to be reduced as quickly as possible. Recently, a new idea of directly using the windings to discharge the capacitor has come forth. This paper proposes a physical energy flow model (EFM) to explain explicitly the winding-based discharge mechanism first. In the EFM, the performance characteristics of the classical winding-based discharge scheme are evaluated, but it is found that the discharge time will not be qualified when the machine rotor inertia is large and the system safe current is small. In order to reject intense voltage surge, a current control algorithm is proposed to bleed the capacitor voltage. Moreover, the proposed current control method shortens the discharge period to below 3 s for the system studied. The proposed discharge algorithm is verified by the experiment that is conducted on a three-phase PMSM drive system.

As the applications of permanent magnet synchronous motor (PMSM) become wide, the demand for highperformance control accuracy of PMSM drive systems is getting higher. It is true that the traditional dual-proportional-integral (PI) loop control strategy is widely used in PMSM drive systems. However, tuning parameters is difficult. In addition, when the system parameters (e.g., motor resistance) change, the output performance of PI controller degrades dramatically. In order to improve the capability of resisting disturbance, this paper presents a sensorless model predictive vector control for PMSM. First, it builds a discrete model of PMSM by using first-order Euler method. Second, a new single-loop model predictive control (MPC) based on back-electromotive force and I-F control is proposed, which simplifies the system structure and control algorithm. Finally, the simulation and experimental results verify that the MPC has high dynamic and steady-state performance.