Inductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors

John Sanchez; Juan Arteaga; Cody Zesiger; Paul Mitcheson; Darrin Young; Shad Roundy

doi:10.3390/s25020309

Inductive Power Transfer Coil Misalignment Perception and Correction for Wirelessly Recharging Underground Sensors

Sensors (Basel). 2025 Jan 7;25(2):309. doi: 10.3390/s25020309.

Authors

John Sanchez¹, Juan Arteaga², Cody Zesiger³, Paul Mitcheson², Darrin Young⁴, Shad Roundy¹

Affiliations

¹ Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
² Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
³ College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA.
⁴ Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA.

PMID: 39860679
DOI: 10.3390/s25020309

Abstract

Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.g., underground) coils. This paper presents an automated methodology to sense misalignments and align IPT coils using robotic actuators and sequential Monte Carlo methods. The misalignment of a Class EF inverter-driven IPT system was modeled by tracking changes as its coils move apart laterally and distally. These models were integrated with particle filters to estimate the location of a hidden coil in 3D, given a sequence of sensor measurements. During laboratory tests on a Cartesian robot, these algorithms aligned the IPT system within 1 cm (0.025 coil diameters) of peak lateral alignment. On average, the alignment algorithms required less than four sensor measurements for localization. After laboratory testing, this approach was implemented with an agricultural sensor platform at the Utah Agricultural Experiment Station in Kaysville, Utah. In this implementation, a buried sensor platform was successfully charged using an aboveground, vehicle-mounted transmitter. Overall, this work contributes to the field of underground sensor networks by successfully integrating a self-aligning wireless power delivery system with existing agricultural infrastructure. Furthermore, the alignment strategy presented in this work accomplishes coil misalignment correction without the need for complex sensor or coil architectures.

Keywords: Monte Carlo methods; agricultural soil sensing; inductive power transfer; machine learning; power transfer coil misalignment; wireless power transfer; wireless underground sensor networks.

Abstract

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