## 1 Introduction  
Wireless power transfer (WPT), as a groundbreaking technology, enables more convenient access to energy in a cordless way. In recent years, WPT has garnered significant interest from researchers and has become increasingly popular in a variety of applications, such as in electric vehicles [1], induction heating [2], lighting [3], and medical implants [4]. In general, wireless power transfer (WPT) systems utilize different compensation topologies to minimize the reactive power produced by the coil inductance. The series-series (SS) topology is commonly that preferred out of various compensation networks because it does not rely on mutual inductance or load resistance [5]. To effectively implement crucial controls in a WPT system, such as maximum energy efficiency tracking (MEET) [6, 7] and impedance matching across a broad load range [8, 9], it is essential to have precise knowledge of critical parameters like the resonant frequency, mutual inductance, and load resistance. Whether it is WPT for electronic devices that should comply with the Qi standard (87 kHz-205 kHz) [10] or WPT for electric vehicles that should meet the SAEJ2954 standard (79 kHz-90 kHz) [11], the resonant frequency will vary across a wide range. Therefore, it is crucial to know the actual resonant frequency of the receiver before running the primary inverter at the corresponding resonant frequency. Hence, the critical parameters of mutual inductance, load resistance, and resonant frequency should be accurately identified before operating the WPT system, as minor deviations in these parameters can result in a significant decrease in performance. Numerous researchers have investigated parameter identification from various perspectives and gained fruitful results. For instance, [PERSON] et al. introduced a technique for detecting load resistance by applying an excitation on the inverter side; then, the load resistance can be calculated based on the time constant of system discharge or the envelope of current decay [12]. While this method is limited to a purely resistive load (e.g., heaters) and a fixed mutual inductance, [PERSON] et al. proposed a genetic algorithm (GA) to search for the mutual inductance (actually replaced by coil spacing) and the resonant capacitance of multiple-stage coils by using frequency sweeping in the range of 450 kHz to 580 kHz [13]. Similarly, [PERSON] et al. presented a front-end parameter monitoring method of mutual inductance and load resistance for two receivers [14]. On the other hand, [PERSON] et al. addressed the scenario of multiple receivers using two-layer adaptive differential evolution (ADE) [15]. [PERSON] et al. presented the parameter identification based on the LCC topology of the WPT system [16]. However, the aforementioned methods that involve wide frequency sweeping are computationally expensive and may only be feasible on a high-end computer rather than on an industrial controller such as the digital signal processor (DSP). To observe the mutual inductance and load resistance during the steady-state operation of a WPT system, [PERSON] et al. proposed a new method by assuming that the parameters of the secondary-side resonant tank are known, in which the precise waveform of the input voltage is required to obtain the amplitudes of the fundamental and third harmonics as well as the harmonics of the current [17]. In addition, it is noted in [18] that continuous frequency modulation of the inverter is necessary to achieve in-phase input voltage and current. However, this method may result in prolonged observation times, system instability, and load variations. Alternatively, [PERSON] et al. and [PERSON] et al. achieved mutual inductance identification within the millisecond range using fast frequency sweeping and hardware phase detection techniques without information on the transmitter and receiver resonators [19; 20]. In recent years, machine learning-based parameter identification methods have attracted attention. [PERSON] et al.