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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Liping Mo & Xiaosheng Wang & Yibo Wang & Ben Zhang & Chaoqiang Jiang, 2024. "Mutual Inductance Estimation of SS-IPT System through Time-Domain Modeling and Nonlinear Least Squares," Energies, MDPI, vol. 17(13), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3307-:d:1429577
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    1. Kai Song & Yu Lan & Xian Zhang & Jinhai Jiang & Chuanyu Sun & Guang Yang & Fengshuo Yang & Hao Lan, 2023. "A Review on Interoperability of Wireless Charging Systems for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-22, February.
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