BTOA-Optimized Self-Tuning Fuzzy 2DOF-PI Cascaded Control for Superior Smooth Performance of BLDC Motor Drives in Electric Vehicles

Source abstract

Brushless DC (BLDC) motors are the preferred traction drive for electric vehicles (EVs), yet achieving precise speed regulation and low torque ripple under varying loads and nonlinear dynamics remains challenging. This paper proposes a cascaded control architecture for BLDC drives under Field-Oriented Control (FOC) in which a Self-Tuning Fuzzy Logic Controller (ST-FLC) continuously adapts the gains of Two-Degree-of-Freedom Proportional-Integral (2DOF-PI) controllers governing both the outer speed loop and the inner d-q current loops. The FLC membership functions and scaling factors are optimized offline by four metaheuristics, namely the Basketball Team Optimization Algorithm (BTOA), Piranha Foraging Optimization Algorithm (PFOA), Pufferfish Optimization Algorithm (POA), and Firefighter Optimization Algorithm (FFO), to minimize an Integral Squared Error (ISE) criterion. MATLAB/Simulink(R2025b) results show that the BTOA-tuned ST-FLC–2DOF-PI controller attains the lowest ISE (2776) and outperforms BTOA-tuned PI, 2DOF-PI, and FLC benchmarks, achieving a 3.5 ms settling time (65% faster than PI), 0.5% overshoot, zero steady-state speed error, the lowest steady-state torque ripple of 15%, and the lowest phase-current THD of 6.28%. Performance is maintained under ±50% parameter variations, and Bode analysis confirms phase margins of 63.4° and 101° for the speed and current loops. Hardware-in-the-Loop validation on an OPAL-RT real-time simulator closely matches the offline simulations, confirming real-time implementability.