A computationally efficient hybrid optimization-based model predictive control for inductive power transfer systems

Model predictive control (MPC) has been actively researched in recent years for power electronics applications due to its intuitive concept, flexibility, and superior dynamic performance. However, current research on MPC for inductive power transfer (IPT) systems is rather limited. Several emerging applications such as dynamic electric vehicle charging, vehicle-to-grid services, and ad hoc power transfer between mobile devices require IPT systems to possess fast dynamic response characteristic. Here, a new MPC method based on computationally efficient hybrid optimization scheme is proposed to meet the needs of these applications. The proposed MPC method adaptively selects the moving discretized control set-based optimization or the newly proposed group-based optimization under different system's states to minimize the number of iterations required for determining the optimum control variable, thus offering the advantage of low computational burden. The paper also proposes a new prediction error compensation scheme that effectively improves the control accuracy of the proposed MPC method. All the proposed works are experimentally verified on a laboratory prototype.