Parameter identification method for a wireless power transfer system based on the Improved Northern Goshawk Optimization Algorithm

This paper proposes a mutual inductance and load parameter identification method using an Improved Northern Goshawk Optimization (INGO) Algorithm after pulse frequency modulation (PFM), and validates the effectiveness of the proposed parameter identification method through a maximum efficiency tracking approach. One of the most challenging aspects of wireless power transfer (WPT) technology for electric vehicles is the variation in the magnetic coupling coefficient between the two coils due to inaccurate parking positions and differences in height. First, a steady-state circuit model of an SS-type compensation network and pulse frequency modulation strategy was established to determine the correlation between system parameters. The INGO was then introduced to convert the parameter identification problem into an optimization problem, enabling the identification of load and mutual inductance parameters. Finally, the proposed identification method was experimentally validated, and the maximum efficiency tracking of the proposed WPT system was achieved through a Sepic impedance matching circuit. The results show that the identification method can maintain high accuracy even under coil displacement and load variation conditions.