Model Predictive Control Based on Constrained Adaptive Particle Swarm Optimization for Dynamic Wireless Power Transfer Systems

In the dynamic wireless power transfer (DWPT) system for electric motor vehicles, it is certain that a reduction in coupling coefficient will reduce the system power as a result of the action of energy receiving inductor coil. And the change rate of coupling coefficient increases with the rise in vehicle speed. The model predictive control (MPC) system, when introduced, is adept at delivering precise regulation and ensuring the DWPT system's stability, even in the face of swift fluctuations in the coupling coefficient. We establish a piecewise affine (PWA) model for DC/DC converter in the DWPT system and propose multiple MPCs according to different affine intervals. In addition, we present a MPC with multi-step compensation to work normally under the delay system. For a quadratic programming (QP) problem converted from MPC, we suggest constrained adaptive particle swarm optimization (CAPSO)[1] to enhance the manipulated variable. The simulation outcomes confirm the efficacy and superiority of the suggested approach within the DWPT system.