In this paper, in order to increase the space for windings and torque capability, a novel yokeless and segmented armature axial field flux switching sandwiched permanent magnet (YASA-AFFSSPM) motor which is appropriate for direct-drive applications is proposed. The proposed motor is composed of single stator and two rotors. Stator winding has unit displacement winding factor. Moreover, two rotors are displaced by $\pi$ (elec.) shift. The unaligned rotors are employed to achieve yokeless and segmented armature structure and also reduce the cogging torque. A three-phase 12/19-pole YASA-AFFSSPM motor is designed. This proposed motor and conventional AFFSSPM are optimized and compared with the conventional AFFSPM. It is shown that the YASA-AFFSSPM motor exhibits higher torque density and greatly lower cogging torque. To evaluate the static and dynamic performance of the YASA-AFFSSPM motor, the sensor less control drives have been applied. A prototype of the optimized YASA-AFFSSPM motor is manufactured and tested.

The axial field flux-switching permanent-magnet machine (AFFSPM) is one of the most efficient machines which is appropriate for high torque and low-speed direct-drive applications. In this paper, the novel axial field flux switching machine with sandwiched permanent magnet (AFFSSPM) is proposed in order to increase torque density and decrease cogging torque. The proposed model equipped with an advanced phase-group concentrated-coil winding to obtain a unity displacement winding factor. Two three-phase 6-stator-slot (S)/10-rotor-pole (P) and 12(S)/19(P) configurations of the proposed model are investigated. Some specific design issues, including the sandwiching pole angle, the stator tooth angle, and the rotor pole angle have been optimized to obtain the maximum torque density and minimum cogging torque by artificial neural network and non-sorting genetic algorithm II. Moreover, the static electromagnetic characteristics are investigated. Compared with the conventional AFFSPM and 6(S)/10(P) AFFSSPM, the 12(S)/19(P) proposed machine shows higher torque density, lower cogging torque, and higher level of fault-tolerant capability. In order to improve the dynamic performance of the AFFSSPM motor, a space vector modulation-direct torque and flux control (SVM-DTFC) scheme is proposed. The AFFSSPM motor exhibits excellent dynamic performance with proposed scheme.

In this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. Based on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation. Based on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results. So far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method.

This paper proposes a multi‐level optimisation design strategy aimed at enhancing the fault‐tolerant capability of a Yoke‐less Axial‐Field Flux‐Switching Permanent Magnet (YASA‐AFFSPM) motor. The research addresses the crucial requirement for robustness and reliability in electric motor systems utilised in safety‐critical applications. By employing a comprehensive multi‐objective optimisation approach, the motor's design is refined at three levels based on the main effect stratification of design variables for the YASA‐AFFSPM motor. The design variables are divided into three levels and optimised, resulting in the attainment of a comprehensive optimal solution that considers multiple design goals, including average torque, cogging torque, and fault‐tolerant capability. The optimal design places a primary emphasis on attaining a substantial level of fault tolerance for both open‐ and short‐circuit faults. This is accomplished by reducing the ratio of mutual‐inductance to self‐inductance. To streamline optimisation, the Kriging model approximates finite element analysis at each level. Furthermore, the effectiveness of the design is showcased through the utilisation of a dual‐channel winding configuration. A prototype is constructed, and experimental validation demonstrates a significant improvement in fault‐tolerance performance, with results indicating enhancements of 14% in average torque and 41% in cogging torque.

This paper proposes a multiobjective hybrid Taguchi genetic algorithm (HTGA) to optimize the speed range of a yokeless axial‐field flux‐switching permanent magnet (YASA‐AFFSPM) motor. HTGA combines Taguchi’s local optimization with the global optimization of traditional genetic algorithms (GAs), facilitating faster and more accurate solutions. The Taguchi method is employed to generate offspring individuals within GA; it inherits parameter characteristics from stronger offspring, saving considerable computation time. The objective is to achieve a motor with low cogging torque, high average torque, and an expanded speed range in the field weakening area. Various parameters of the motor, such as the split ratio, stator axial length, pole angles, PM arc, and number of conductors per slot, are selected as optimization variables. The optimization constraints include the field‐weakening rate, saliency rate, cogging torque, and average torque. The optimized motor parameters are determined, and the speed range before and after optimization is evaluated. Cosimulation analysis using a 3‐D finite element method (FEM) is performed under no‐load and full‐load conditions to compare the motor’s speed regulation range. The optimized motor exhibits a maximum speed that is almost 1.5 times higher than the initial design, with improvements of 11.3% in average torque and 9% in cogging torque. Experimental results compared to 3‐D FEM simulations demonstrate the superior performance of the optimized motor in terms of speed, torque, power, and efficiency.