Artificial intelligence-enhanced DTC command methods used for a four-wheel-drive system

Abstract

This paper presents an artificial intelligence direct torque control (DTC) method for an electric vehicle (EV) drive system. The architecture of the proposed electric vehicle is that of four wheels each with an induction motor (IM). A comparative study of the different torque and speed controllers proposed in this paper is made. An electronic differential is used to control the speed of each wheel as well as a variable master-slave control (VMSC) for the management of the magnetic quantities because the motors on the same side are fed by the same converter. This study allows highlights the performance of the propulsion system in terms of dynamics and safety of the vehicle and better stability. The different controllers are implemented by the MATLAB/Simulink software and the simulation results obtained show better flexibility in the control of the vehicle. It is worth noting that direct torque control with fuzzy logic (DTFC) performs better than DTC associated with neural networks in terms of a time reduction increase of 1.47%, an overshoot of less than 5.33, and a static steady-state error close to zero.