Design and Simulation of an Optimized Hybrid Fuzzy-PI Control Strategy for BLDC Motor in Electric Vehicle Applications

Abstract

This paper presents the design, development, and simulation of an optimized hybrid Fuzzy–PI control strategy for the speed regulation of Brushless DC (BLDC) motors in electric vehicle (EV) traction applications. The proposed controller integrates a Mamdani-type fuzzy inference system (FIS) with a conventional proportional–integral (PI) regulator to achieve rapid transient response, superior robustness to load disturbances, and minimized torque ripple. The model is implemented in MATLAB/Simulink and evaluated under various operating conditions, including step-speed commands and sudden load changes. Performance metrics such as rise time, settling time, overshoot, torque ripple, and steady-state error are analyzed. Simulation results demonstrate that the hybrid controller achieves a 55% improvement in rise time and a 70% reduction in overshoot compared with traditional PI control, with nearly zero steady-state error. These findings confirm the suitability of the proposed method for high-performance EV drive systems where adaptive speed control, reliability, and efficiency are critical.