Adaptive Neuro Fuzzy Inference System Based BLDC Motor for Electric Vehicle

This work focuses on the design and simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS)–based controller for Brushless DC (BLDC) motors used in electric vehicles (EVs). The primary objective is to improve speed regulation and enhance energy efficiency under varying load and driving conditions. MATLAB/Simulink was used to model the motor drive system and train the ANFIS controller using motor dynamic response data. The performance of the ANFIS controller is compared with conventional PID and Fuzzy controllers, demonstrating improved robustness, stability, and overall efficiency. This research contributes to intelligent motor control strategies for energy-efficient EV propulsion systems.