This paper proposes a novel outer-rotor flux-switching permanent-magnet (OR-FSPM) machine with specific wedge-shaped magnets for in-wheel light-weight traction applications. First, the geometric topology is introduced. Then, the combination principle of stator slots and rotor poles for OR-FSPM machines is investigated. Furthermore, to demonstrate the relationship between performance specifications (e.g., torque and speed) and key design parameters and dimensions (e.g., rotor outer diameter and stack length) of OR-FSPM machines at preliminary design stage, an analytical torque-sizing equation is proposed and verified by two-dimensional (2-D) finite-element analysis (FEA). Moreover, optimizations of key dimensions are conducted on an initially designed proof-of-principle three-phase 12-stator-slot/22-rotor-pole prototyped machine. Then, based on 2-D-FEA, a comprehensive comparison between a pair of OR-FSPM machines with rectangular- and wedge-shaped magnets and a surface-mounted permanent-magnet (SPM) machine is performed. The results indicate that the proposed OR-FSPM machine with wedge-shaped magnets exhibits better flux-weakening capability, higher efficiency, and wider speed range than the counterparts, especially for torque capability, where the proposed wedge-shaped magnets-based one could produce 40% and 61.5% more torque than the rectangular-shaped magnets-based machine and SPM machine, respectively, with the same rated current density (5 A/mm ² ). Finally, the predicted performance of the proposed OR-FSPM machine is verified by experiments on a prototyped machine.

Flux-switching permanent magnet machine typically suffers from relatively high cogging torque due to its special doubly salient structure and high air-gap flux density, causing undesired torque ripples, as well as acoustic noise and vibration, especially at low speeds. In this paper, an analytical expression of cogging torque is derived based on a magnetomotive force-permeance model, through which the optimal design parameters and dimensions, such as combinations of stator slots and rotor poles, skewing angle, stator tooth width, rotor tooth width, and magnet thickness, can be determined. Based on the derived analytical model, two novel approaches for cogging torque reduction are proposed, i.e., asymmetric magnetomotive force and asymmetric permeance, respectively, and consequently, the optimal designs are also acquired analytically. However, it is also found that the optimal choice of the proposed techniques depends on the combinations of stator slots and rotor poles of the machines. Finally, the effectiveness of the proposed approaches is verified by both finite element analytical predictions and experimental results.

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.

Brushless modular spoke type permanent magnet (MSTPM) machine has exhibited prior electromagnetic performance over conventional surface-mounted PM machines for in-wheel traction system in electric vehicles (EVs). However, analysis of thermal behavior of MSTPM machines is rather insufficient, even accurate thermal analysis is of significant importance due to poor heat dissipation condition inside the wheel hub. Conventionally, the losses produced in electrical machines by EM prediction is equivalent to the heat source in normal thermal analysis, and only the resultant thermal behavior is investigated. However, the reaction of temperature rising on the EM performance is neglected. In this paper, a bi-directional coupled electromagnetic-thermal analysis method is proposed and carried out by a combination of lumped parameter thermal network (LPTN) model and finite element method (FEM), where both steady-state temperature distribution and transient-state temperature rise are investigated for MSTPM machines. By finite number of iterations between magnetic and thermal fields, the electromagnetic performance and thermal behavior can be predicted more accurately due to the coupling effect considered. The coupled model-predicted results are verified by 3D-FEM and experimental measurement, which shows that the proposed method has advantages in both computational efficiency and accuracy as well as can be applied to other electrical machines.

To address both single open-switch fault (OSF) and arbitrary two OSFs of a five-phase permanent magnet synchronous motor (PMSM) drive, a generalized fault-tolerant model predictive current control method is proposed in this article. Firstly, the postfault currents under OSFs are analyzed in the stationary frame based on the Fourier method. Then, to maintain torque output capacity and alleviate torque ripple, the optimal reference currents in fault scenarios are deduced with the principle of rotating magnetic motive force undisturbed. Further, the obtained phase currents are transformed into the rotating frame and applied in the cost function for the voltage vector optimization. Thus, the torque under post-fault operations is guaranteed and the torque ripples are suppressed. Finally, various experiments are conducted to validate the feasibility of the generalized fault-tolerant control method of five-phase PMSM drives.

This paper proposes a novel modular spoke-type permanent-magnet machine for in-wheel traction applications. First, the topology and the operating principle are briefly introduced. Then, an analytical model of the proposed machine is deduced based on conformal mapping. A three-phase 48-slot 52-pole modular spoke-type permanent-magnet (PM) machine is designed based on the deduced mathematical model where the dimensions and power source are constrained the same as those of a commercial three-phase surface PM machine in electric motorcycles. Electromagnetic performance comparisons among the proposed machine, the commercial machine, and a conventional spoke-type PM machine are conducted based on finite-element analysis (FEA) with respect to magnetic field, back electromotive force (EMF), flux-weakening capability, torque capability, machine efficiency, etc. The results indicate that the proposed machine has an improved torque, a higher efficiency, and an extremely better flux-weakening capability than the conventional in-wheel machine. Finally, a prototype machine is manufactured and tested to verify both the analytically and FEA predicted results.