Energy-efficient speed control of PMSMs in electric vehicles using adaptive sliding gmode and D-HHO metaheuristics

Electric vehicles (EVs), in practical use, need motor-control methods that can give quick speed response, low torque ripple, and better energy use under changing road, load, and battery conditions. Permanent Magnet Synchronous Motors (PMSMs) are commonly preferred for EV traction due to their high torque density, compact design, and good efficiency, though their performance may reduce under nonlinear dynamics, disturbances, parameter variation, and State-of-Charge (SOC) changes. This paper proposes a hybrid nonlinear SMC supported by Dynamic Harris Hawks Optimisation (D-HHO). D-HHO acts as a supervisory tuner and updates SMC gains during simulation using recent speed error, motor response, and control effort. A lightweight predictive layer adjusts the boundary before expected speed changes, while an SOC-aware loop moderates control action to reduce unnecessary battery use. The controller is evaluated in MATLAB/Simulink using a UDDS-based EV speed profile and representative PMSM operating points. Compared with PID and fixed-gain SMC, the proposed method improves speed tracking, torque smoothness, current distortion, torque ripple, and energy utilisation.