A Review on an Intelligent Controller for an EV Propulsion System Using Different Optimization Techniques

Abstract

Transportation and vehicle travel are essential for socioeconomic growth because they consent communities and enterprises to operate around the world. However, major portions of the transportation sector predominantly dependent on gas-based vehicles, resulting in elevated air pollution and emission of greenhouse gas. vehicles powered by battery (EVs) provide a potential solution to transportation issues. Consequently, a strong traction motor control system is crucial to the optimization of the electric drive system. As a result, the purpose of this work is to discuss novel control approaches implemented in the traction motor system in EVs. Direct Torque management (DTC) and Indirect Field Oriented Control (IFOC) are popular control strategies because they provide enhanced management of the Induction Motor (IM) and Permanent Magnet Synchronous Motor (PMSM) used in most EVs. This paper describes different Metaheuristic Algorithms integrated with electric motor drives aim to overcome challenges in estimating real-time vehicle conditions and reduce torque ripple. An overview of the performance characteristics, strengths, and weaknesses of various motor types and control strategies are reviewed Finally, a comprehensive evaluation of various motor drives in electric vehicles using different metaheuristic algorithm-based control strategies highlights their significance and potential outcomes.