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<p>This study presents a comprehensive comparative analysis of three prominent speed control strategies Sliding Mode Control (SMC), Proportional-Integral-Derivative (PID), and the Brain Emotional Learning-Based Intelligent Controller (BELBIC) for Brushless DC (BLDC) motors in electric vehicle (EV) applications. MATLAB/Simulink simulations, combined with a software-in-the-loop (SIL) setup on an Opal-RT simulator equipped with a Spartan-3 FPGA processor, are used to rigorously evaluate each controller. Key performance metrics include disturbance rejection capability, adaptability to varying operating conditions, dynamic response characteristics, total harmonic distortion (THD), and torque ripple. The results demonstrate that BELBIC consistently outperforms SMC and PID controllers, offering faster dynamic response, enhanced adaptability, reduced torque ripple, and lower THD under diverse conditions. While SMC exhibits greater robustness and faster response than PID, the adaptive learning mechanism of BELBIC provides superior handling of parameter uncertainties and dynamic load changes typical in EV drive systems. These findings highlight BELBIC’s potential to enhance the efficiency, reliability, and driving comfort of electric vehicles, making it a strong candidate for next-generation BLDC motor speed control over traditional and sliding mode approaches.</p>

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Intelligent Control Strategies for BLDC Motors in Electric Vehicles: Unveiling the Optimal Balance of Speed, Stability, and Efficiency

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Ganneboina Raju,
  • G. N. Srinivas

摘要

This study presents a comprehensive comparative analysis of three prominent speed control strategies Sliding Mode Control (SMC), Proportional-Integral-Derivative (PID), and the Brain Emotional Learning-Based Intelligent Controller (BELBIC) for Brushless DC (BLDC) motors in electric vehicle (EV) applications. MATLAB/Simulink simulations, combined with a software-in-the-loop (SIL) setup on an Opal-RT simulator equipped with a Spartan-3 FPGA processor, are used to rigorously evaluate each controller. Key performance metrics include disturbance rejection capability, adaptability to varying operating conditions, dynamic response characteristics, total harmonic distortion (THD), and torque ripple. The results demonstrate that BELBIC consistently outperforms SMC and PID controllers, offering faster dynamic response, enhanced adaptability, reduced torque ripple, and lower THD under diverse conditions. While SMC exhibits greater robustness and faster response than PID, the adaptive learning mechanism of BELBIC provides superior handling of parameter uncertainties and dynamic load changes typical in EV drive systems. These findings highlight BELBIC’s potential to enhance the efficiency, reliability, and driving comfort of electric vehicles, making it a strong candidate for next-generation BLDC motor speed control over traditional and sliding mode approaches.