<![CDATA[This paper presents the application of a modified firefly algorithm (MFA) for tuning the proportional-integral (PI) speed controller of a brushless direct current (BLDC) motor drive, targeting improved overall dynamic performance of the motor drive system for electric vehicle (EV) applications. The controller’s effectiveness is evaluated under two variants of the New European Driving Cycle (NEDC) to replicate real-world driving scenarios. To validate the effectiveness of the proposed approach, a comparative study is carried out with two widely used optimization techniques, such as the standard firefly algorithm (FA) and particle swarm optimization (PSO). Comparative analysis reveals that the MFA-tuned controller delivers superior speed tracking accuracy, with significantly reduced speed error, speed ripple, and copper losses, when compared to controllers optimized using the standard firefly algorithm (FA) and particle swarm optimization (PSO). These improvements enhance both the energy efficiency and operational stability of the motor drive. Furthermore, the result of the experiment shows that the proposed controller demonstrates strong adaptability under varying load and speed conditions, positioning it as a robust solution for both electric vehicles and industrial motor control applications.]]>
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