Multi-Objective Optimal Cloud Model Design of Vehicle-to-Grid Connected Systems Based on the Multiple Performance Characteristic Index Method

Abstract

In this paper, a statistical cloud model was proposed for optimal design of the proportional integral derivative (PID) controllers used in current control of vehicle-to-grid connected inverter systems with PID parameters. By collecting the effective control factors and noise factors from a cloud data base, the cloud model can minimize both the reactive power and the total harmonic distortion for the single-phase full-bridge vehicle-to-grid connected system. The multi-objective optimal solution is obtained by using statistical fuzzy-based response surface methodology with multiple performance characteristics index. The testing results showed the validity of the proposed cloud model. It is verified that the statistical cloud model can increase the performance of the single-phase full-bridge vehicle-to-grid connected system in practical vehicle-to-grid applications in the Internet of Things.

Keywords

• vehicle-to-grid connected system
• full-bridge inverter
• PID controller
• statistical cloud model
• multiple performance characteristics index (MPCI)
• fuzzy-based response surface methodology
• orthogonal particle swarm optimization (OPSO)
• Internet of Things (IOT)