Maximum Efficiency Tracking of a Three-coil Inductive Power Transfer System for Wireless EV Battery Charging Applications

Abstract

Inductive power transfer (IPT) systems are emerging as a superior technology compared to their conductive counterparts due to having more advantages in applications like electric vehicles, biomedical devices, industrial automation, and household appliances. Research interest in IPT systems having one or more intermediate coils has increased in recent years because of their benefits in terms of efficiency and transmission distance over traditional two-coil systems. However, both types of systems suffer from efficiency degradation due to load variation and coil misalignment since maximum efficiency only occurs at a narrow operating region. Although much research has been done on efficiency optimization of two-coil systems to avoid this issue, work done on IPT systems with three or more coils is not adequate. Conventional efficiency optimization methods employ different load matching techniques or maximum efficiency tracking based on perturbation and observation (P&O) algorithm. Load matching techniques generally require estimating mutual inductance and load resistance. P&O, on the other hand, being an iterative algorithm, is slow in operation and results in slow dynamics. To address these issues, this research proposes a hybrid model predictive control and perturbation and observation (MPC-P&O) based double-sided control technique to optimize the efficiency of a three-coil S-S-S compensated wireless battery charging system. Constant-current and constant-voltage (CC-CV) charging algorithm for the battery will be implemented using PI controllers at the secondary side. The proposed MPC-P &O controller expects to achieve optimum system efficiency and better dynamic response under load variation and coil misalignment. Simulation and experiments will be performed to validate the proposed control method and its efficacy.