In safety- and precision-critical control scenarios for permanent magnet synchronous motors (PMSMs), the external spontaneous disturbance causes unexpected speed drop. The disturbance occurs without routine, so it cannot be modeled specifically. The large speed drop and slow response speed cause a reduced life of the machines driven by PMSMs. Therefore, it is crucial to implement a method that can lead the controller to learn the effects caused by disturbances. To this end, this paper proposes a novel approach based on the basic structure of a backpropagation neural network (BP) for adaptive real-time adjustment in motor control. Regarding the lack of explainability of BP in existing methods, the electric motor physics is embedded into the BP (BP-PHY) gradient update part to enlarge the range of stability. To overcome the shortage of a potentially unstable output of neural network (NN), the learning parameter of NN is tailored based on the stability theory and motor physics. Finally, the proposed methods are implemented into simulations and experiments. The recovery time and the speed drop after disturbance decrease to nearly one-half compared to the basic controller of PMSM, while the control stability of the NN is ensured.

Existing model-free predictive control (MFPC) methods are primarily based on mature controller design formulas, with various observers added to these controllers. Currently, there is a lack of modifications to the mechanism of model-free predictive control itself. This letter starts with the original formulation of the ultra-local model and redesigns a model-free predictive control method from the fundamental formula level. First, a term based on an exponential-like function is proposed, modifying the proportion of the controller's error term, called exponential MFPC. Subsequently, a switching control concept is introduced into the controller design, facilitating better integration of the existing method with the new one, ensuring system performance across different error intervals. Ultimately, the effectiveness of the proposed switched ultra-local MFPC is demonstrated by the experimental result of the superiority in the loading aspect with smaller speed overshoot and speed oscillations.

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

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.

This paper proposes an analytical model of the double-stator spoke-type permanent magnet vernier machine (DSSTVM) using the subdomain method (SDM), which can be used to calculate the magnetic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace’s and Poisson’s equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the corresponding magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model’s effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.