Identification of Mutual Inductance and Load in WPT System Based on Artificial Neural Network

In the field of magnetically coupled resonant wireless power transmission (MCR-WPT), the dual-parameter identification of mutual inductance and load parameters plays an important role in the development of effective wireless charging solutions for a range of technical devices. The extant parameter identification methods are beset with shortcomings, including slowness, low precision, parameter coupling, and a complex derivation process. This paper puts forth a mutual inductance and load dual-parameter identification method based on neural networks for series-series compensated wireless power transfer (SS WPT) system. By leveraging the non-linear characterisation capabilities of neural networks and integrating the principles of dual-parameter identification, a model for identifying the parameters of a magnetically coupled resonant wireless power transmission system can be constructed. This approach enables the identification process to be completed rapidly and with high precision.