Inductor design of magnetic resonance coupled circuits for secured wireless power transfer

Sun, Hongru; Gao, Feifei; Zhu, Fengchao; Zhao, Junhui; Li, Yunzhou

doi:10.1109/iccchina.2017.8330496

Cited by 2 publications

(1 citation statement)

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“…To prevent unauthorized users from accessing the system, in [2] the expected receiver power was maximized by optimizing the current of transmitters to meet the power constraint of the transmitter's transfer power and unexpected receivers' power, which realized the security power transfer. On this basis, to increase the expected received power, in [3] the system design was converted into an optimization problem, which maximized the expected receiver power by optimizing the turns of the coil in the magnetic resonance circuit, and used semi-definite relaxation to optimize the solution. Consider that the recent wireless charging devices need wireless charging protocol to guarantee the security power transfer, which means that the transmitter needs to communicate with the receiver before energy transfer.…”

Section: Introductionmentioning

confidence: 99%

Zero-Forcing Beamforming Energy Efficiency Optimization for the Security Control of Wireless Power Transfer System

Xu¹,

Chen²,

Qiu³

et al. 2021

PIER M

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This paper proposes a zero-forcing beamforming design for the energy efficiency optimization of the magnetic resonance based wireless power transfer system with multiple transmitter coils, which aims to secure energy transfer control. A scheme based on beamforming technology is proposed to prevent unauthorized users from accessing the system, which builds a beamforming model consisting of multiple transmitter coils, a target receiver, and a non-target receiver to simulate the actual system. Then to optimize the proposed system's energy efficiency while constraining the target receiver's energy, spectral efficiency, and transmitter's power, the proposed beamforming model is constructed as an optimization problem. To solve this non-convex nonlinear fractional programming problem, the Dinkelbach algorithm is used for fractional conversion, and then the zero-forcing constraints are equivalently replaced. Finally, two solutions of the nonlinear solution and closed-form solution are derived. The simulation results show that the energy efficiency optimization strategies of zero-forcing beamforming with the two derived solutions can satisfy the design requirements.

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Section: Introductionmentioning

confidence: 99%