In this paper, a novel axial flux double stator switched reluctance motor (AFDSSRM) is presented and optimized for electric vehicles application. AFDSSRM adopts the axial arrangement of double stator and inner rotor structure with full-pitch winding configuration. The flux generated by the two stators cancel each other at the unaligned position, then a low unaligned inductance barely affected by saturation is achieved, which is the primary advantage of the AFDSSRM. First, the topology and power equation of the motor are presented briefly. Due to a large number of dimensional parameters of the proposed structure, comprehensive sensitivity analysis is used to classify the design parameters into strong-sensitive and weak-sensitive classes, and a multi-layer optimization approach is adopted for the variables of both classes. The response surface method combined with the multiobjective genetic algorithm is employed to optimize the strong-sensitive variables, while the Taguchi algorithm is applied to optimize the weak-sensitive variables. Moreover, the 3D finite element model is established to analyze the electromagnetic characteristics of the motor. Finally, a prototype motor is manufactured, and the experimental results verify the effectiveness of the proposed structure and optimization method.
Design and Multiobjective Optimization of A Double-stator Axial Flux SRM with Full-pitch Winding Configuration
Musolino A.;Sani L.;
2022-01-01
Abstract
In this paper, a novel axial flux double stator switched reluctance motor (AFDSSRM) is presented and optimized for electric vehicles application. AFDSSRM adopts the axial arrangement of double stator and inner rotor structure with full-pitch winding configuration. The flux generated by the two stators cancel each other at the unaligned position, then a low unaligned inductance barely affected by saturation is achieved, which is the primary advantage of the AFDSSRM. First, the topology and power equation of the motor are presented briefly. Due to a large number of dimensional parameters of the proposed structure, comprehensive sensitivity analysis is used to classify the design parameters into strong-sensitive and weak-sensitive classes, and a multi-layer optimization approach is adopted for the variables of both classes. The response surface method combined with the multiobjective genetic algorithm is employed to optimize the strong-sensitive variables, while the Taguchi algorithm is applied to optimize the weak-sensitive variables. Moreover, the 3D finite element model is established to analyze the electromagnetic characteristics of the motor. Finally, a prototype motor is manufactured, and the experimental results verify the effectiveness of the proposed structure and optimization method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
