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Lightweight Autonomous Underwater Vehicle WPT System with Axial Misalignment Tolerance for Constant Current and Voltage Control Strategy

  • Northwestern Polytechnical University Xian
  • Xidian University

Research output: Contribution to journalArticlepeer-review

Abstract

In a wireless power transfer (WPT) system for autonomous underwater vehicles (AUVs), load variations and coupler misalignment critically affect constant current-constant voltage (CC-CV) output. Furthermore, lightweight design remains crucial for AUVs. After docking with the base station, axial misalignment is dominant for the AUV. Therefore, a WPT system using bipolar pad (BP) transmitters to compensate for axial misalignment is proposed. Subsequently, a mathematical model of primary-side DC information and output characteristics for the proposed system is established, enabling CC-CV output under varying load resistances and axial coupler misalignment. To enhance output prediction accuracy, a nonlinear prediction model utilizing a backpropagation neural network (BPNN) was developed. This model can fit components that are difficult to model precisely, such as eddy current losses and harmonics. It predicts the AUV side output via primary side DC information, saving the AUV from requiring additional power electronics and communication devices while simplifying the primary-side data acquisition and processing. This method simultaneously realizes load-independent CC-CV output, axial offset tolerance, and lightweight AUV design. Simulation and experimental results show good startup performance, steady-state accuracy, and transient response. An experimental prototype achieved CC-CV output under ±40 mm axial offset with loads ranging from 40-400 Ω, exhibiting maximum output errors below 3.4%/5.2% in CC/CV modes. During startup or changes in load resistance and axial position, the tuning time consistently remained below 1.7 s.

Keywords

  • Wireless power transfer
  • autonomous underwater vehicle
  • axial misalignment tolerance
  • back propagation neural network
  • constant current-constant voltage output

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