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The authors proposed a unidirectional, high‐gain antenna loaded with the artificial magnetic conductor (AMC) back‐cavity, which generates the reflection phase of 0° for gain enhancement in a low profile. The working bandwidth of the AMC unit cell is extended to 5.1–7.4 GHz (36.5%) by applying the fractal technology. The radiation performance improvement, wider bandwidth and increased gain are obtained for the composite antenna mounted on the fractal AMC reflector with the distance of 2 mm (0.04 wavelength at the central frequency of 6 GHz). The simulated results indicated that the operational bandwidth of the proposed antenna ranges from 4.7 to 8 GHz (52.0%). Besides, an average gain enhancement of 4 dB as compared to the original antenna without AMC back‐cavity is achieved over the entire operating band. In addition, the proposed antenna is manufactured and tested, the measured results coincide well with the simulated counterparts.

The present work aims at comparing different coupling coils by taking into account sources of uncertainty for static inductive power-transfer (SIPT) systems. Due to the maximum transmission efficiency for the SIPT system related to the mutual inductance between coils, the key point here is to make use of a sparse polynomial chaos expansion (PCE) method to analyze the mutual inductance between the transmitter and the receiver. A fast postprocess-sensitivity analysis allowed the identification of which source of uncertainty was the most influential factor to the mutual inductance for different coupling coils. Furthermore, in view of the relationship between the maximum transmission efficiency and the ratio of the length of wires of a coil and the mutual inductance, circular coupling coils should be recommended for SIPT systems.

The paper addresses the uncertainty quantification of physical and geometrical material parameters in the design of wireless power transfer systems. For 3D complex systems, a standard Monte Carlo cannot be directly used to extract statistical quantities. So, surrogate models based on Kriging or polynomial chaos expansions are built to study the impact of variable parameters on the radiated magnetic field and efficiency. Such fast prediction of uncertainties in the parameters of the system can improve the design of inductive power transfer systems taking into account human exposure recommendations and variability of the parameters.

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