Electric vehicle manufacturers face a persistent challenge in motor control: induction motor parameters—particularly stator and rotor resistance—vary significantly during operation due to temperature changes and thermal stress, degrading the performance of standard Field-Oriented Control systems. This paper addresses that problem through a hybrid approach combining FOC with hysteresis current control and online adaptive resistance estimation.
The research integrates a Current-Based Model Reference Adaptive System (CB-MRAS) that continuously estimates both stator and rotor resistance values during real-time operation. By processing voltage and current measurements through dual computational models, the system detects parameter drift and automatically compensates the FOC algorithm, maintaining accurate rotor flux orientation and speed tracking even as motor characteristics change.
Key advantages demonstrated include smoother torque response, more reliable speed regulation, and stable flux control across variable operating conditions. The system remained effective even under challenging scenarios: inverter switching delays, hysteresis band fluctuations, aggressive HWFET driving cycles, and regenerative braking events.
Experimental validation proved particularly significant. Tests at low speed (300 rpm) with a 20 percent intentional stator resistance increase and elevated temperatures (70°C) confirmed the approach works in practice, not merely in simulation. The adaptive estimator tracked resistance changes in real time without requiring manual recalibration or prior knowledge of motor thermal behavior.
This work has direct implications for EV efficiency and performance. More accurate motor control reduces energy losses during acceleration and regeneration, extending driving range. Improved speed tracking enhances driver experience during low-speed maneuvers and highway cruising. The method integrates readily into existing EV motor control architectures, making adoption feasible without significant hardware redesign.
As electric vehicles proliferate and operate across wider temperature ranges and driving conditions, technologies that maintain control stability and efficiency become increasingly valuable for both consumer satisfaction and grid demand management.
