PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

Abstract

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied in electric vehicles is a crucial task. In this paper, the detection of ITSC faults in stator winding for PMSMs is carried out by means of the Extended KALMAN Filter (EKF) algorithm combined with the Fuzzy Logic Estimator (FLE). To estimate the motor parameters, including the rotor position and speed, the EKF algorithm uses the measured stator currents and voltages beside the stator resistance, which is calculated in advance using fuzzy logic and fed to the EKF. The change behaviors of the estimated parameters were then used to detect short circuit faults in the PMSM. Using Matlab/Simulink, the proposed FL-EKF algorithm was implemented and tested on a faulty PMSM controlled by Field Oriented Control (FOC). The observation of a perfect estimation of the stator resistance through the simulation helps to precisely detect the failure, and that demonstrates the sensitivity and robustness of the proposed approach.

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

“…Extended Kalman filtering (EKF) offers a versatile estimation methodology for estimating the state of a nonlinear system through linearized processing. This approach enables a more accurate estimation of the state of an unconstrained nonlinear model [7,8], which is why EKF is widely used in sensorless controllers for PMSM [9][10][11]. Nevertheless, due to the lack of estimation ability for constrained nonlinear systems, the effectiveness of EKF on the sensorless control of PMSM may be limited in practical applications.…”