Advanced Control Strategies for BLDC Motor Speed Regulation in Electric Vehicles
Keywords:
Brushless DC (BLDC) motors, proportional integral derivative (PID) controller, Fuzzy PID controller, ANN controllerAbstract
The growing adoption of electric vehicles (EVs) has intensified the need for high-performance, energy-efficient, and reliable motor control systems. Brushless DC (BLDC) motors are widely preferred in EV propulsion due to their high torque density, reduced maintenance, and superior dynamic response. However, achieving precise speed regulation under varying load, road, and battery conditions remains a critical challenge. This study investigates advanced control strategies such as Field-Oriented Control (FOC), Model Predictive Control (MPC), Adaptive PID, and ann Controllers for improving the speed regulation of BLDC motors used in EVs. The performance of each method is evaluated in terms of response time, steady-state error, robustness to disturbances, and energy efficiency. Simulation results demonstrate that intelligent and model-based controllers outperform conventional PID control by providing smoother torque production, reduced ripples, and improved adaptability under real-world operating conditions. The work highlights the potential of hybrid control approaches that combine AI-based decision-making with classical control to achieve optimal speed regulation and enhanced vehicle performance. These findings contribute to the development of next-generation EV drive systems that are more efficient, responsive, and reliable.
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