Load and Mutual Inductance Recognition Method for LCC-S MCRWPT Systems Based on Improved Dung Beetle Optimization Algorithm

One of the key issues in improving the performance of magnetically coupled wireless power transfer (WPT) systems is the identification of load and mutual inductance parameters. This study proposes an improved dung beetle optimization algorithm for identifying load and mutual inductance in an LCC-S type magnetically coupled WPT system. By neglecting the influence of higher-order harmonics in the system, a steady-state circuit model is established, providing a mathematical description of the system's circuit parameters and state variables. On this basis, the parameter identification problem of the system is transformed into an optimization problem, and an improved dung beetle optimization algorithm is employed to identify the system's load and mutual inductance parameters. Through parameter identification, further control can be achieved, such as efficiency maximization and adjustment of the DC-DC circuit duty cycle for maximum efficiency tracking based on the identified mutual inductance and load. This identification method does not require additional circuits or related control, thereby reducing circuit complexity and system size. Simulation and experimental results verify the feasibility and effectiveness of this identification method.