Adaptive Tabu Search-optimized Speed Control Of A Bldc Motor Drive For Electric Vehicle Applications

Abstract

High-efficiency Propulsion Drives Are Essential For Advancing Modern Electric Vehicle (EV) Powertrains. This Paper Presents A Speed-control Architecture For Brushless DC (BLDC) Motors Using An Adaptive Tabu Search (ATS) Algorithm To Optimize Proportional-integral-derivative (PID) Controller Parameters. Standard Empirical PID Tuning Methods Often Fail To Deliver Adequate Robustness Against The Nonlinear Dynamics, Abrupt Road-load Changes, And Fluctuating Operating Conditions Inherent To EV Drivetrains. To Address This, The ATS Algorithm Systematically Searches The Multi-dimensional Parameter Space (Kp, Ki, Kd) While Explicitly Enforcing Physical Control Voltage Limits To Prevent Actuator Saturation And Digital Over-modulation. MATLAB/Simulink Simulations Under Severe Dynamic Load Profiles Demonstrate That The Proposed ATS-optimized Controller Effectively Eliminates Steady-state Tracking Error, Accelerates Settling Time, And Mitigates Startup Overshoot Compared To Conventional Tuning Techniques.

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