Enhancing Electric Vehicle Drive System Using Model Predective Control
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Date
2025
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Publisher
Saudi Digital Library
Abstract
This study presents the design and simulation of a Finite Control Set Model Predictive Control (FCS-MPC) approach for a three-phase squirrel-cage induction motor in the stationary reference frame. The proposed algorithm eliminates the need for modulation schemes by directly selecting inverter voltage vectors through cost function evaluation. The cost function includes torque tracking, rotor flux regulation, and switching effort to determine the optimal control action. MATLAB/Simulink simulations under various dynamic conditions validate the effectiveness of the controller in achieving rapid torque response, stable flux regulation, and computational efficiency. The results confirm that the FCS-MPC method improves speed and torque performance compared to conventional control strategies such as Field-Oriented Control (FOC) and Direct Torque Control (DTC), making it a promising technique for future electric vehicle drive systems.
Description
This master’s thesis investigates the development and optimization of an advanced electric vehicle (EV) drive system using Finite Control Set Model Predictive Control (FCS-MPC). The research aims to improve torque response, flux stability, and computational efficiency for induction motor drives used in EV applications. The project involves the complete design and simulation of the control strategy in MATLAB/Simulink, covering inverter modeling, dynamic motor analysis, and cost function formulation.
Comprehensive simulation tests were carried out to evaluate steady-state and transient performance, including speed steps and load disturbances. The findings demonstrate that the proposed FCS-MPC approach offers faster dynamic response and reduced torque ripple compared to traditional Field-Oriented Control (FOC) and Direct Torque Control (DTC) techniques. This work contributes to the advancement of intelligent and energy-efficient electric vehicle control systems and provides a foundation for future experimental implementation on real-time platforms.
Keywords
Model Predictive Control, Electric Vehicle Drive System, Induction Motor, Finite Control Set, Predictive Torque Control, Inverter Modeling, MATLAB/Simulink Simulation
Citation
Althobaiti, F. F. (2025). Development of Electric Vehicle Drive System Using Model Predictive Control. Master’s thesis, Queen Mary University of London.