A Robust Approach for Inter-Turn Fault Detection of PMSM Used for Autonomous Vehicles

In autonomous vehicles technology, one huge challenge is the continuous in-vehicle performance assessment and monitoring. Reliable methods for fault-detection, fail-awareness, prediction of degradation of hardware are pivotal to ensure safe operation at any time and under any weather condition. In this contribution we focus on a robust detection of certain electric motor faults as part of the safety-critical drive-by-wire actuation. Especially inter-turn short circuit (ITSC) faults caused by electrical insulation failures in the stator windings of typically used permanent magnet synchronous motors (PMSM) can lead to shorts to ground resulting in undesired behaviours such as oscillations in torque and localized heating. To detect and isolate ITSC faults, we propose a model-based sliding mode approach. An online parameter estimation in each stator phase of a three-phase PMSM prototype is carried out. Robustness, feasibility, and effectiveness of this scheme are elaborated by benchmarking with the state-of-the-art solution, the extended Kalman filter (EKF). Numerical simulation results, which confirm that the performance is significantly improved in the noisy case using the proposed strategy without full information of rotor speed and position, are given.