SUN Anran, XIA Chenyang, YANG Ziyue, et al. Parameter Identification Technology of Multi-frequency and Multi-load MCR-WPT Systems Based on HM-PWM Control[J]. 2025, 45(20): 8189-8201.
DOI:
SUN Anran, XIA Chenyang, YANG Ziyue, et al. Parameter Identification Technology of Multi-frequency and Multi-load MCR-WPT Systems Based on HM-PWM Control[J]. 2025, 45(20): 8189-8201. DOI: 10.13334/j.0258-8013.pcsee.240846.
Parameter Identification Technology of Multi-frequency and Multi-load MCR-WPT Systems Based on HM-PWM Control
The multi-frequency and multi-load (MFML) magnetic coupling wireless power transfer (MCR-WPT) system is difficult to model and analyze due to its complex structure
diverse frequency
and numerous parameters. In this paper
a multi-parameter identification method is proposed for MFML MCR-WPT systems controlled by Hybrid Modulation PWM (HM-PWM). The method constructs the primary current RMS equation and the phase difference equation between the primary voltage and current at each load frequency point
and realizes the effective identification of each load and mutual inductance parameter based on non-dominated sorting genetic algorithm Ⅱ-trust region (NSGA Ⅱ-TR). Firstly
the structure and parameter identification principle of the MFML MCR-WPT system controlled by HM-PWM are analyzed. Secondly
the system model and parameter identification equation are established
and the equation is solved precisely based on NSGA Ⅱ-TR algorithm. Thirdly
the influence of the system parameters on the identification accuracy is analyzed
and a method to improve the identification accuracy is proposed according to the impedance characteristics of the system. Finally
an experimental platform is built to validate the theory. The experimental results show that the proposed method can achieve high precision identification of each load and mutual inductance parameter in the MFML MCR-WPT system
and the maximum parameter identification error is less than 5.93%. At the same time
the maximum error of parameter identification can be further reduced to 2.38% by using the improvement method to enhance the parameter identification accuracy.