In safety-critical control for permanent magnet synchronous motors (PMSMs), the overshoot after adding a spontaneous load is a crucial metric, leading to the unexpected motion of driving equipment, which induces potential unsafe problems. Therefore, it is necessary to develop a control method that effectively reduces overshoot in PMSMs. Recognizing the nature of overshoot effects, a data-driven approach, Gaussian process regression (GPR), is employed to generate the prediction. With a focus on maintaining the advantage of the GPR method while preserving the physical properties of PMSM, an overshoot reduction-inspired motor physics embedded GPR method (OR-MPE-GPR) is proposed. Inspired by the shape of the overshoot, the squared exponential (SQE) kernel function is chosen for GPR. Furthermore, by using sufficient conditions to achieve stability, the dynamic stable range and static stable range of the updating rate are derived to guarantee the stability of the proposed machine learning control algorithm. Finally, comprehensive simulations and experiments compared with the state-of-the-art methods are conducted, showcasing the good performance of the proposed method in reducing overshoot while preserving static performance within a stable region.

Electromagnetic-thermal analysis is crucial to guarantee the axial flux motors’ (AFMs) reliable operation as their compact size and poor heat dissipation conditions can lead to thermal limits. However, the conventional coupled analysis is extremely time-consuming, especially in 3D analysis. To this end, a fast semi-analytical electromagnetic-thermal coupling calculation framework is proposed that couples a harmonic modeling method (HMM) with a lumped parameter thermal network (LPTN), incorporating heat transfer mechanisms including conduction, convection, and radiation. In the electromagnetic field analysis part, an order-reduced electromagnetic model is constructed using the HMM considering the nonlinearity of the iron core. Based on that, a bi-directional electromagnetic-thermal coupling analysis is realized, the variation of electromagnetic properties with temperature, including copper conductivity, permanent magnet (PM) remanence, and the permeability of the iron core, are incorporated into the coupling analysis. Notably, the stator teeth are finely segmented in the electromagnetic model to accurately capture magnetic saturation effects under varying temperature conditions. Ultimately, the reliability and accuracy of the proposed method are verified through the finite element method (FEM) and prototype experiments. It is demonstrated that compared with FEM, the proposed framework has a satisfactory calculation efficiency and saves 98.4% of the computation time than FEM, making it suitable for preliminary motor design and optimization considering multiphysics influence.

In this paper, a new axial-type magnetic gear with an auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AMGs, it has a higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of the proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterparts in RMGs, the AFS-AMG achieves a small size. Then, the geometrical parameters of the AFS-AMG are optimized to obtain better electromagnetic performance, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Permanent magnet Vernier machines (PMVMs) can achieve high torque at low speeds due to the flux modulation effect, but their low power factor limits the applications in in-wheel motors and ship propulsion systems. This article proposes a five-phase PMVM with a hybrid star–pentagon winding and a yokeless spoke-type rotor employing trapezoidal-shaped magnets, significantly improving both power factor and torque density. The hybrid winding configuration enhances the power factor by combining the benefits of star and pentagon windings, increasing the working harmonic contribution while maintaining excellent harmonic suppression. Furthermore, the yokeless spoke rotor utilizes oppositely magnetized trapezoidal magnets with a large cross section to strengthen the flux focusing effect, thereby boosting magnet flux linkage and torque output. Finite element analysis (FEA) was conducted under identical overall dimensional constraints to compare the proposed machine with two existing five-phase machines that have been optimally designed. The proposed machine exhibits a higher proportion of effective armature flux and enhanced magnetic flux linkage. As a result, it delivers at least 19.6% higher torque than the other comparative models, albeit with a slight reduction in power factor. Finally, a prototype is fabricated and experimentally tested, validating that the proposed machine exhibits an output torque of 24.9 Nm and up to a power factor of 0.883.