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The purpose of this study is to search for an optimal core shape that is robust against misalignment between the transmitting and receiving coils of the wireless power transfer (WPT) device. During the optimization process, the authors maximize the coupling coefficients while minimizing the leakage flux around the coils to ensure the safety of the WPT device.

In this study, a novel topology optimization method for WPT devices using the geometry projection method is proposed to optimize the magnetic core shape. This method facilitates the generation of bar-shaped magnetic cores because the material distribution is represented by a set of elementary bars.

It is shown that an optimized core shape, which is obtained through topology optimization, effectively increases the net magnetic flux interlinked with the receiving coil and outperforms the conventional core.

In the previous topology optimization method, the material distribution is represented by a linear combination of Gaussian functions. However, this method does not usually result in bar-shaped cores, which are widely used in WPT. In this study, the authors propose a novel topology optimization method for WPT devices using geometry projection that is used in structural optimization, such as beam and cantilever shapes.

This study aims to realize a sensorless metal object detection (MOD) using machine learning, to prevent the wireless power transfer (WPT) system from the risks of electric discharge and fire accidents caused by foreign metal objects.

The data constructed by analyzing the input impedance using the finite element method are used in machine learning. From the loci of the input impedance of systems, the trained neural network (NN), support vector machine and naive Bayes classifier judge if a metal object exists. Then the proposed method is tested by experiments too.

In the test using simulated data, all of the three machine learning methods show high accuracy of over 80% for detecting an aluminum cylinder. And in the experimental verifications, the existence of an aluminum cylinder and empty can are successfully identified by a NN.

This work provides a new sensorless MOD method for WPT using three machine learning methods. And it shows that NNs obtain high accuracy than the others in both simulated and experimental verifications.