A Predictive Finite Speed-Set MRAS for Robust Sensorless Control of PMSM Drives in Electric Vehicles Under Driving Cycle

Model reference adaptive system (MRAS)-based techniques are widely used for sensorless speed estimation in permanent magnet synchronous motor (PMSM) drives. However, conventional MRAS approaches rely on PI/PID controllers in the adaptation mechanism, which require careful tuning and may suffer from performance degradation under varying operating conditions. This paper proposes a tuning-free finite speed-set model reference adaptive system (FSS-MRAS) for sensorless speed estimation in electric vehicle (EV) applications. The suggested design methodology substitutes the traditional adaptive controller design with a speed-set predictive selection approach, hence removing the need for controller parameter tuning and making the scheme more straightforward to implement. To validate the performance of the FSS-MRAS algorithm, a comprehensive EV model is simulated according to the FTP-75 driving cycle, which provides very dynamic and realistic test conditions. Simulation results prove that the suggested algorithm guarantees accurate speed estimation in all operational scenarios, including low-speed, high-speed, and high-dynamics modes. It is also demonstrated that the estimated value matches the actual value with negligible errors at the steady-state condition and stays bounded around the real value within ±40 rpm during transient dynamics conditions. Moreover, compared with the conventional MRAS scheme, the proposed approach eliminates the need for PI controller tuning while maintaining accurate and stable speed estimation under the highly dynamic operating conditions of the FTP-75 driving cycle. Additionally, the proposed scheme proves stable performance without any oscillation or divergence issues through the whole driving cycle.