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In this paper, the multi-objective topology optimizations of wireless power transfer (WPT) devices with two different coil geometries are proposed for obtaining the designs with good balance between transfer efficiency and safety. For this purpose, the proposed method adopts the normalized Gaussian network (NGnet) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). In addition, the optimization under the different constraint on ferrite volume is carried out to verify its influence on optimization results. It has been shown that the proposed method successfully provides the Pareto solution to the design problem of the WPT device.

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This paper presents the machine learning-based detection of foreign metal object for the wireless power transfer device including differential coils. To test the proposed method, the differential voltages are computed using finite element method for about 1500 cases with and without an aluminum cylinder at driving frequency of 85 kHz considering misalignment between the primal and secondary coils. It has been shown that gradient boosting decision tree and random forests classifier have the accuracy over 90% when input voltages and differential voltages are inputted together.