This paper presents an improved artificial bee colony (I-ABC)-based offline optimization framework for tuning a fractional-order proportional–integral–derivative (FOPID) controller to regulate the speed of a permanent magnet synchronous motor (PMSM) in electric vehicle (EV) applications. The proposed approach addresses the practical challenge of achieving high dynamic performance and robustness while maintaining deployability in embedded EV motor control systems. The I-ABC algorithm incorporates adaptive neighborhood shrinking, elitist solution retention, and local refinement mechanisms to enhance global exploration and ensure reliable convergence in the five-dimensional FOPID parameter space. Unlike existing ABC- or GA-based PMSM control approaches that rely on online adaptation or hybrid co-simulation, the proposed I-ABC–FOPID framework performs complete offline tuning within MATLAB/Simulink, enabling direct deployment in embedded EV motor controllers with minimal computational burden. The novelty of this work lies in the integration of the I-ABC algorithm with offline FOPID tuning, which consistently outperforms conventional PID, GA–FOPID, and GA–RBL–FOPID controllers. Extensive simulation studies conducted at multiple speed setpoints (300, 600, and 900 rpm) demonstrate that the proposed controller achieves up to 75% reduction in peak overshoot, nearly 50% improvement in rise and settling times, and steady-state error below 0.3%. In addition, the controller maintains clean voltage and current waveforms, minimizes cumulative error indices (ISE, IAE, and ITAE), and exhibits strong robustness against load disturbances. These results confirm that the proposed I-ABC–tuned FOPID controller provides a high-performance, reliable, and energy-efficient solution suitable for real-time PMSM speed control in electric vehicle applications.
G. et al. (Thu,) studied this question.