Electromagnetic Performance Prediction of a Double-Rotor Flux-Switching Motor Based on General Air-Gap Equivalent Algorithms Model

In this paper, a dynamic nonlinear magnetic network model is proposed to predict the electromagnetic performance of a double-rotor flux-switching motor (DRFSM) in which a general air-gap equivalent model considering the irregular field distribution of double air gaps and the dynamic variation of air gaps composition is involved. The double air-gap distribution and inner-outer machine back-EMF of the DRFSM are efficiently predicted by the proposed computation model. The results are validated by the finite element analysis (FEA) and experiments, where good agreements prove the accuracy and effectiveness of the proposed model.