An integrated fault detection and identification system for permanent magnet synchronous motor in electric vehicles

Abstract

This paper proposes an efficient and integrated fault detection and identification system for power converters and permanent magnet synchronous motor in electric vehicles. Switching faults of power converters (single, double and triple switching faults), electrical and mechanical faults of the permanent magnet synchronous motor (bearing fault, stator electrical faults) are considered. Fault detection is done using Clarke transformed (α-β) three-phase current analysis. Features are extracted from the current signals and artificial neural network (ANN) is used for the fault identification. Using motor current signature analysis and by selecting simple and suitable features, the system can detect and distinguish between overall faults of power converters and permanent magnet synchronous motor in an electric vehicle; it requires no complex calculations. The proposed system is designed in MATLAB/Simulink. The system is tested under different fault scenarios and performance is evaluated. The simulation results have proved that the proposed system can detect and identify overall faults of power converters and permanent magnet synchronous motor easily and effectively with no need for complex calculations and techniques.