Abstract:

The global adoption of electric vehicles (EVs) is on the rise as a sustainable alternative to oil- and gas-based vehicles. Brushless direct current (BLDC) motors are particularly popular for industrial and traction applications, as they require low error in steady-state and transient responses for smooth acceleration. This paper presents the design and control of a BLDC motor for speed control during acceleration and deceleration, considering error as a key factor in the MATLAB/Simulink environment. Proportional-integral (PI) and fuzzy controllers are commonly used for motor control to improve steady-state and transient performance, thereby reducing error. In this study, the PI and adaptive neuro-fuzzy inference system (ANFIS) controllers are designed and compared for a 5-kW, 48-V, and 100-Amp BLDC motor in EV applications. The results demonstrate that the ANFIS controller enhances the dynamic performance of the BLDC motor and improves other operating characteristics such as rise time, settling time, peak overshoot percentage, and vehicle response in terms of speed and distance.