Mutual Inductance Estimation of SS-IPT System through Time-Domain Modeling and Nonlinear Least Squares

Liping Mo, Xiaosheng Wang, Yibo Wang, Ben Zhang and Chaoqiang Jiang ()
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Liping Mo: Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
Xiaosheng Wang: Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
Yibo Wang: Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
Ben Zhang: Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
Chaoqiang Jiang: Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China

Energies, 2024, vol. 17, issue 13, 1-14

Abstract: Inductive power transfer (IPT) systems are pivotal in various applications, relying heavily on the accurate estimation of mutual inductance to enable system interoperability discrimination and optimal efficiency tracking control. This paper introduces a novel mutual inductance estimation method for Series-Series IPT (SS-IPT) systems, utilizing time-domain modeling combined with nonlinear least squares. Initially, the time-domain model of SS-IPT systems is developed by deriving its ordinary differential equations (ODEs). Subsequently, the mutual inductance is estimated directly from these ODEs using a nonlinear least-squares approach. This approach necessitates only primary-side information, eliminating the need for communication, supplementary equipment, or frequency scanning. The simplicity and directness of using collected real-time data enhance the practical applicability of our approach. The effectiveness of the proposed method is substantiated through simulations and experimental data. Results demonstrate that the estimation accuracy of our method remains more than 95.0% in simulations and more than 92.5% in experimental data.

Keywords: inductive power transfer system; parameter estimation; nonlinear least square (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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