Enhancing Torque Smoothness in BLDC Motors with Built-in DC-DC Converter via Bitterling Fish Optimization Algorithm

Brushless DC (BLDC) motors are efficient and robust electric motors with fewer moving parts, but their application is often limited by torque ripple, which arises from current variations between the entering and exiting phases during commutation. This study aims to minimize torque ripple in BLDC motors integrated with a DC-DC converter. The proposed optimization method utilizes the Bitterling Fish Optimization (BFO) Algorithm to effectively control torque error and speed, addressing the torque ripple caused by current variations during commutation. The proposed method is implemented using the MATLAB working environment and compared with various existing methods like Spider Web Algorithm (SWA), Improved Tunicate Swarm Optimization Algorithm (ITSA), and Harris Hawks Optimizer with Black Widow Optimization (HHO-BWO). The results indicate that the proposed method achieves a reduced torque ripple rate of 9.64, significantly lower than the rates of 17.32, 11.20 and 22.19 for ITSA, HHO-BWO and SWA respectively. Additionally, the proposed approach exhibits low error of 0.168, outperforming the existing methods errors of 0.287, 0.195 and 0.311. These findings demonstrate that the BFO algorithm effectively minimizes torque ripple more than existing optimization techniques, providing a promising solution for enhancing the performance of BLDC motors.