Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic

This study investigates the use of a closed-loop speed control approach based on fuzzy logic for brushless DC (BLDC) motors in Electric Vehicle (EV) applications. The primary objective is to overcome the drawbacks of traditional control techniques by improving dynamic performance, response time, and stability under changing load conditions and parameter uncertainty. Nonlinearities, load fluctuations, and transient overshoots are common problems for traditional PID controllers, which results in suboptimal performance of EV propulsion systems. A state-space modelling technique for the BLDC motor is used in this study to address these issues, incorporating a Fuzzy Logic Controller (FLC) for accurate speed control. The superiority of FLC over PID controllers is demonstrated by a comparison study that was verified by simulation and hardware implementation. The results show that FLC produces smooth speed transitions, no overshoot, and zero steady-state error with a settling time of only 0.05s, as in contrast to 0.1s for the PID controller. Under load fluctuations, the FLC's torque response stays constant at about 1.05 Nm, however the PID controller shows noticeable oscillations and a larger torque ripple. Additionally, FLC guarantees smooth speed regulation throughout a broad range (1500-3000 rpm), greatly increasing motor lifespan and energy efficiency. When compared to the PID controller, the experimental validation shows that FLC performs robustly in real-time EV settings, exhibiting smoother speed transitions, faster disturbance rejection, and improved adaptability. According to these results, FLC is a better option for BLDC motor speed control in EV applications, guaranteeing effective propulsion, less mechanical stress, and more driving stability. As a potential control strategy for upcoming EV technologies, the proposed strategy not only improves energy utilisation but also strengthens system reliability.