Optimized control strategies for BLDC motor in sustainable energy systems: enhancing stability and performance through intelligent controllers, filtering techniques and internal model control

The challenge of achieving reliable and efficient control of Brushless DC Motors (BLDCMs) in electric and hybrid vehicles, where system nonlinearities and dynamic disturbances hinder traditional control methods, is analysed in this article. It compares conventional and intelligent control techniques through both time- and frequency- domain analyses. Metrics such as speed, torque, back Electromotive Force (EMF), and phase currents are evaluated under different conditions. Although standard PID and PI controllers are common, their tuning becomes complicated by nonlinear behaviors and limited flexibility. The study explores advanced methods like Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and hybrid strategies that combine FLC and SMC with PI control to enhance transient and steady-state performance. Filtering techniques like the Extended Kalman Filter (EKF) and Particle Filter (PF) are used to improve state estimation and noise reduction, with PF outperforming EKF by approximately 30%. The Internal Model Control (IMC), including its two-degree-of-freedom (2-DoF) variant, is also analyzed for its ability to enhance gain margin and stability in non-minimum phase systems. These hybrid control schemes offer better disturbance rejection, faster settling times, and greater robustness, showing promise for increasing the reliability of BLDCM-driven sustainable transportation systems.