AI-Enhanced Control and Simulation of BLDC Motors for Autonomous Vehicle Applications

Due to their efficiency, reliability, compact size, and wide operating range, Brushless DC (BLDC) motors have been swarming most applications in the electric vehicle. The demand for precision and stability is significantly imperative for control over BLDC motors when it comes to autonomous vehicles (AVs), as it cares about these aspects much because such affects the performance, energy efficiency, and safety. This paper proposes an innovative AI-enhanced control strategy for the BLDC motor, integrated with real-time simulation to optimize their operation in AVs. The research focuses on developing an effective control framework using advanced machine learning algorithms such as reinforcement learning and neural networks in order to dynamically adapt to different driving conditions, thus improving system performance. Simulation results are presented to demonstrate the applicability of the proposed AI-driven methodology in order to improve efficiency, speed control, and fault tolerance of the motor in various scenarios of AV operations. The authors finally provide real implementation details of the Scilab-based simulation environment with actual-time evaluation and optimization of the BLDC motor control. Results unveiled improvements in response time of the motor setup but, very much related overall energy efficiency, that go a long way toward improving autonomous electric vehicles (EVs).