Software-in-the-loop simulation for design and optimization of control algorithms to reduce torque ripple in BLDC motors for EV applications

Abstract In electric vehicle (EV) applications, torque ripple significantly impacts performance, reliability, and ride comfort, rendering it a critical issue for Brushless Direct Current (BLDC) motors. Addressing this issue necessitates the integration of rigorous validation procedures with sophisticated control mechanisms. This study presents an analytical framework employing Software-in-the-Loop (SIL) simulation for the systematic design and optimisation of control algorithms, such as Proportional-Integral-Derivative (PID), Fuzzy Logic Control (FLC), Sliding Mode Control (SMC), and Model Predictive Control (MPC), aimed at minimising torque ripple in BLDC motors. The novelty lies in implementing SIL as a high-fidelity, model-based validation platform for iterative development and refinement of control algorithms under different operating load conditions. Unlike conventional design approaches, the proposed framework leverages SIL adaptability to optimise control logic before Hardware-in-the-Loop (HIL) or real-time deployment, significantly reducing development risk and time. The MPC is configured with a one-step-ahead current prediction, a prediction horizon of 10, a control horizon of 3, weighting factors of 1 and 0.01, and a 100 μs sampling interval, optimising speed regulation, current tracking, and torque control bandwidth while ensuring robustness under parameter uncertainties. Simulation results confirm MPC’s superiority over traditional controllers, achieving a rise time of 0.012 s, a steady-state error of 0.001%, a settling time of 0.018 s, and a peak overshoot of 0.058%. Moreover, a 29.02% reduction in torque ripple was observed compared to baseline methods. These findings underscore SIL-based MPC’s potential for scalable, high-performance BLDC motor control in next-generation EV drive systems.