This article employs surrogate models for large-scale high-speed (HS) electrical machine optimization to reduce heavy computational burden caused by the finite-element method (FEM) based on nondominated sorting genetic algorithm II. Three artificial neural networks, namely, multilayer perceptron (MLP), support vector regression (SVR), and generalized regression neural network (GRNN), and the classical Kriging model are developed based on the ${K}$ -fold crossing validation method. To find out the most suitable surrogate model, a HS permanent magnet synchronous machine (PMSM) applied in electrically assisted turbocharger (EATC) considering its multiphysics characteristics is optimized by different models. A detailed comparison is provided in terms of prediction accuracy and modeling time consumption. The optimal design is verified to guarantee the accuracy of optimization results. The key contributions of this article include an automatic HSPMSM optimization process is proposed, where complete modeling and tuning method as well as a reasonable comprehensive evaluation metric of different surrogate models are conducted. It is found that invalid samples from the initial dataset contain useful information to improve prediction accuracy. Hence, an entire surrogate process should benefit from a pretrained classifier. Also, it reveals that MLP, SVR, and Kriging model can greatly improve the design optimization effectiveness with high accuracy, whereas GRNN is not suitable for this specific optimization scenario.

To obtain the best comprehensive performance of the flux-switching permanent magnet (FSPM) machines, six cogging torque suppression techniques based on rotor-side including skewing, step-skewing, notching, pairing, right-angle chamfering, and cos-chamfering rotors are investigated in this paper. Firstly, suppression mechanisms of the rotor techniques are classified into two categories and analyzed through general air-gap field modulation theory (AFMT). Then, based on finite element analysis (FEA), cogging torque waveforms of the FSPM machine by each technique are predicted to evaluate cogging torque suppression effectiveness, the reasons and differences of cogging torque reduction are explained from the perspective of air gap magnetic field harmonics. Next, electromagnetic performances are calculated and compared, including saliency ratio ( R_sa ), back-EMF, torque, and rotor robustness. Further, a system-level theoretical analysis for cogging torque suppression of FSPM machines is conducted to disclose the common nature of different techniques. According to six key factors, namely, cogging torque suppression rate ( R_ct ), per unit saliency ratio ( R_sa ( p.u .)), fundamental proportion ( P_f ) of back-EMF, torque retention rate ( R_tr ), torque ripple suppression rate ( R_trs ), and torque ripple robustness ( R_ob ( T_rip )), a more comprehensive rotor design idea (or general design guideline) is provided for machine researchers and designers. Finally, prototype experiments concerning a 12s/10p FSPM machine with four rotor techniques are conducted. The results can provide a significant reference for cogging torque suppression techniques of FSPM machines.

In the high-speed flux-switching permanent magnet (FSPM) machine, the AC copper loss issue in the armature winding conductor is very prominent, due to the high-frequency leakage magnetic field in stator slot, which seriously affects the machine performance. To model and calculate AC copper loss more accurately, a coupled electric circuit-magnetic field model is constructed based on 2D-finite element method (FEM), and consequently the influence of modeling methods and winding types on AC copper loss calculation of a 12-slot/10-pole (12/10) 54.7kW/10krpm FSPM machine is investigated. Firstly, 10 simplified AC copper loss calculation models are established to deal with the problem of large consumption of calculation time and computer resources caused by the excessive number of wires, small size of winding conductors, high precision modeling, and mesh refinement. Secondly, the influences of voltage-/current-source and steady/rotating rotor are considered, and the 10 models are evaluated in terms of computing time and accuracy. Thirdly, based on the preferred model from the 10 models, the effects of frequency, round/flat wires and wire sizes on AC copper loss are further studied. Fourthly, to investigate the influence of flux leakage at the end of winding on AC copper loss, a 3D AC copper loss calculation model with different axial lengths of winding end space is established. Fifthly, the change process of AC copper loss and transient temperature of winding under short time over-load condition are obtained combined with the bidirectional coupling model between electromagnetic and thermal field method through multiple iterations of electromagnetic loss and temperature. Finally, the stator modules of round and flat copper wires are manufactured respectively, and an experimental modular device is built to measure the AC copper loss, which confirms the improved accuracy of the predictions by the proposed model.

This article proposes a new axial dual-stator flux-switching permanent magnet (FSPM) machine for high-speed applications. The machine adopts a fractional slot distributed winding, which has a higher winding factor, efficiency, and torque density. First, the high-speed FSPM machine topology and operating principle are introduced. By employing the winding complementarity principle, the unfavorable even harmonics of a single stator in the FSPM machine can be counteracted, resulting in an essentially sinusoidal permanent magnet (PM) flux-linkage as well as effectively reduced cogging torque and torque ripple. Second, based on the 3-D finite element method (FEM), both the no-load and on-load electromagnetic performances are obtained. Compared with the other three FSPM machines, the advantages of the proposed machine are highlighted. Finally, a high-speed FSPM prototype is manufactured and a test platform is built to measure the no-load and on-load performances, which verifies the theoretical analysis and simulated results.

Accurate prediction of core losses plays an important role in the design and analysis of flux-switching permanent magnet (FSPM) machines, especially during high-speed and high-frequency operation. Firstly, based on the numerical method, a high-frequency core loss prediction method considering a DC-bias magnetization component and local hysteresis loops as well as the harmonic effect is proposed. Secondly, the magnetizing characteristics of the silicon steel sheet and, consequently, the core loss of the electrical steel used as the core lamination are measured. Then, the loss coefficient of each core loss component is obtained by the data fitting tool. Based on the proposed method, the stator and rotor core losses of a three-phase, 12-stator-slot, and 10-rotor-pole (12/10) FSPM machine with different soft iron materials and driving modes are calculated. Finally, the results of the numerical method are verified by conventional finite element analysis.

Canned induction motors (CIMs) are particularly important for high vacuum application due to its low cost, simple design, and mechanically robust. Herein, this study reveals a mechanism of asymmetrical stator axes (ASAs) for CIMs and proposes six coefficients to describe ASA for CIMs under asymmetrical stator phase's windings. Using winding theorem, this study derived the electromagnetic field and driving characteristics in the healthy condition and ASA conditions for a 1.9 kW CIM, and analysed further using mathematical formulas and finite‐element models. Both simulation and experimental results show good agreement, and significant variation was found in total harmonic distortion, the amplitude of air‐gap flux density, and driving characteristics of CIMs. Further, investigation of back‐electromotive force and stator current showed their efficacy as a diagnosis signature to detect the fault of ASA in CIMs. The obtained conclusions can serve as a valuable reference to design a high‐performing CIMs control system.