Adaptive Speed Control of BLDC Motors Based on Fuzzy Inference System Using a PWM Strategy for Electric Vehicles

Along with the development of increasingly advanced technology, innovations continue to develop, one of which is in the field of transportation. In line with the rapid advancement of technology, electric vehicles (EVs) have gained significant attention due to their environmental and performance advantages. Among the EV components, the Brushless Direct Current (BLDC) motor stands out due to its high efficiency and minimal maintenance. This paper proposes a speed control system for BLDC motors based on the Fuzzy Inference System (FIS). The system was evaluated through acceleration, deceleration, and energy efficiency tests over a 2400-meter track. Results indicate that the FIS-based controller achieved smoother speed transitions and higher energy efficiency (380.95 km/kWh) compared to open-loop control (358.2 km/kWh). These findings suggest that FIS can enhance the performance and reliability of electric vehicle drivetrains.