Design and Multiobjective Optimization of a Double-Stator Axial Flux SRM With Full-Pitch Winding Configuration

• Published in: IEEE transactions on transportation electrification Vol. 8; no. 4; pp. 4348 - 4364
• Main Authors: Yu, Fengyuan, Chen, Hao, Yan, Wenju, Pires, Vitor Fernao, Martins, Joao F. A., Rafajdus, Pavol, Musolino, Antonino, Sani, Luca, Aguirre, Miguel Pablo, Saqib, Muhammad Asghar, Orabi, Mohamed, Li, Xiaodong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-12-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Axial flux switched reluctance machines (SRMs); Configurations; Design optimization; Design parameters; Electric motors; Electric vehicles; electromagnetic performance; Electromagnetic properties; Finite element method; finite-element analysis; full-pitch winding; Genetic algorithms; Inductance; Magnetic cores; Magnetic flux; Multilayers; multiobjective optimization; Multiple objective analysis; Optimization; Parameter sensitivity; Reluctance motors; Response surface methodology; Rotors; Sensitivity analysis; Stator cores; Stator windings; Stators; Three dimensional models; Topology; Winding; Windings
• ISSN: 2332-7782; 2577-4212; 2332-7782
• DOI: 10.1109/TTE.2022.3173938

In this article, a novel axial flux double-stator switched reluctance motor (AFDSSRM) is presented and optimized for electric vehicle applications. AFDSSRM adopts the axial arrangement of double-stator and inner rotor structure with a full-pitch winding configuration. The flux generated by the two stators cancels each other at the unaligned position, and 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- and weak-sensitive classes, and a multilayer 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 3-D 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 the optimization method.