Application of Artificial Intelligence Techniques on Double-Squirrel Cage Induction Motor for an Electric Vehicle Motorization

Abstract

Electric Vehicles (EVs) are complex electromechanical systems described by nonlinear models and therefore, their control design and analysis is not an easy task. Double-Squirrel Cage Induction Motor (DSCIM) has the advantages of driving complex loads which require high starting torque and low starting current. This paper presents a speed control comparison between the PI controller and the advanced techniques of control based on the Artificial Neural Networks (ANN) and Fuzzy Logic (FL) in order to be applied for an electric vehicle motorization. The simulation results are numerically validated by using the MATLAB/Simulink universe; they highlight the robustness properties of the different control strategies based on field-oriented control technique.