A Particle Swarm Optimization Parameter Identification Algorithm based on Model Predictive Control of Wireless Power Transfer System

In wireless power transfer systems, mutual inductance and load parameters play important roles. They affect the efficiency and output ability of the system and often change in real situations. Therefore, in order to achieve better real-time control of the system and improve its dynamic performance, it is necessary to identify these parameters. In this paper, a new parameter identification algorithm is proposed. This algorithm combines the model predictive control algorithm with the particle swarm optimization algorithm to achieve the desired control target based on the principle of the magnetic coupling resonance. The obtained control signal is input into the pulse density modulation to realize the control of the inverter and rectifier. Compared with the original algorithm, it has two distinct characteristics. One is that its dynamic performance is better and its scope of work is wider, and the other is that it has a simple hardware structure and is easier to implement. The simulation are carried out in order to verify the feasibility of the proposed algorithm.