Performance Assessment of Sensorless Controlled Permanent Magnet Synchronous Motors for Electric Vehicles Under Realistic Drive Cycles

Abstract

Permanent magnet synchronous motors (PMSMs) are widely employed in electric vehicles (EVs) owing to their high efficiency, high power density, and reliability. Although sensors may raise costs, the accurate estimation of both rotor position and actual speed is crucial to achieve high motor performance. This paper presents a comparative assessment of sensorless PMSM drive control using field-oriented control (FOC) and model predictive control (MPC) under various representative automotive drive cycle conditions. A model reference adaptive system (MRAS) observer is employed to estimate rotor position and speed, and the complete drive system is simulated via MATLAB/Simulink R2025b. Performance is assessed using representative synthetic urban and highway drive cycle segments, designed to capture key dynamic features (stop-and-go transients, steady-state cruising, and road grade variation) analogous to those found in standardized cycles such as WLTP, considering speed tracking error, torque ripple, and current/estimation quality indicators. The results indicate that FOC–MRAS provides robust steady-state behavior and reliable estimation under low-speed urban segments, whereas MPC–MRAS achieves faster transient response and improved current regulation in higher-speed segments at the expense of increased computational complexity. The reported findings elucidate the practical trade-offs between robustness and predictive capability and provide simulation-based guidance for selecting sensorless control strategies for PMSM-based EV traction drives.