Model Predictive Control Design for Electric Vehicle Based on Improved Physics-Inspired Optimization Algorithms

Abstract

Abstract: This paper introduces the mathematical model of the leader-follower electric vehicle (EV). Consequently, the system was analyzed to obtain stability and performance. Model Predictive Control (MPC) is also proposed to fix the EV system issues. Moreover, two optimization algorithms are applied to optimize the performance of the MPC: electrically charged particle optimization (ECPO) and improved chaotic electromagnetic field optimization (ICEFO). The MPC scheme is based on the Adaptive Cruise Control System (ACCS), applied to two vehicles: the leader and follower. In this context, the simulation results of both optimization methods with the MPC scheme are presented in the result section. Finally, a comparison is made to show the proposed controller’s effectiveness with the improved optimization algorithms. Also, the ACC electric vehicle tracking system was achieved at 98% with the reference input.

Keywords

• Adaptive Cruise Control System
• Electric Charged Particles Optimization
• Improved Chaotic Electromagnetic Field Optimization
• Leader-Follower
• Model Predictive Control