Hybrid KarcıFANN–EEFO controller for adaptive speed control of BLDC motors in electric vehicles

Electric vehicles (EVs) have gained significant attention in recent years and are rapidly emerging as a sustainable alternative to fossil fuel-based transportation due to their efficiency and environmental benefits. Maximizing energy efficiency and increasing driving range are the main goals of EV development, and both of these goals rely significantly on improving motor performance. The Brushless Direct Current (BLDC) motor is the best option for EVs among different motor types because of its strong performance, high efficiency, and excellent speed and position control capabilities. To achieve accurate and adaptive speed regulation, this study proposes a Hybrid Karcı Fractional Artificial Neural Network (KarcıFANN) controller. The KarcıFANN architecture integrates fractional calculus with artificial neural networks, providing enhanced learning adaptability and memory characteristics for dynamic system control. To further optimize the KarcıFANN’s performance, Electric Eel Foraging Optimization (EEFO) is employed for weight and bias tuning. The EEFO algorithm improves convergence speed and prevents premature stagnation during training, ensuring optimal controller performance. Simulation results obtained in the MATLAB/Simulink environment demonstrate superior performance over conventional controllers, achieving a settling time of 0.0321 s and a rise time of 0.0201 s under the high-inertia condition, together with low overshoot and accurate reference-speed tracking. Consequently, the integration of KarcıFANN with EEFO presents a robust and efficient solution for BLDC motor speed control, marking a substantial advancement in intelligent EV propulsion systems and contributing to the evolution of sustainable and responsive transportation technologies.