Abstract

Due to the urgent desire for a fast, convenient, and efficient battery charging technology for electric vehicle (EV) users, extensive research has been conducted into the design of high-power inductive power transfer (IPT) systems. However, there are few studies that formulate the design as a multiobjective optimization (MOO) research question considering both the aligned and misaligned performances and validate the optimal results in a full-scale prototype. This article presents a comprehensive MOO design guideline for highly efficient IPT systems and demonstrates it by a highly efficient 20-kW IPT system with the dc–dc efficiency of 97.2% at the aligned condition and 94.1% at 150-mm lateral misalignment. This achievement is a leading power conversion efficiency metric compared to IPT EV charging systems disseminated in today’s literature. Herein, a general analytical method is proposed to compare the performances of different compensation circuits in terms of the maximum efficiency, voltage/current stresses, and misalignment tolerance. An MOO method is proposed to find the optimal design of the charging pads, taking the aligned/misaligned efficiency and area/gravimetric power density as the objectives. Finally, a prototype is built according to the MOO results. The charging pad dimension and total weight, including the housing material, are 516*552*60 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /25 kg for the transmitter and 514*562*60 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /21 kg for the receiver. Correspondingly, the gravimetric, volumetric, and area power density are 0.435 kW/kg, 581 kW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , and 69.1 kW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The measured efficiency agrees with the anticipated value derived from the given analytical models.

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