Research on Inter-Turn Short Circuit Fault Diagnosis Method of Switched Reluctance Motor for Electric Vehicles

Addressing the challenges of a complex operating environment, slow recognition response times, and the high cost associated with inter-turn short circuit faults (ISCF) in switched reluctance motors (SRM), this article introduces a novel fault diagnosis method based on the interacting multiple model (IMM) algorithm. Equivalent circuit models corresponding to different operating states of ISCF in each phase of the SRM were developed. The Kalman filter was employed to estimate the model's state, with the residual signal utilized for fault localization and detection. Simulation and experimental results demonstrate that the proposed fault diagnosis method significantly mitigates the impact of intricate scenarios, enhances the anti-interference capability in diagnosing SRM stator winding ISCF, and enables precise and rapid identification of fault information.