电动汽车无线充电系统采用感应线圈随磁场变化进行异物检测

IF 2.4 Q2 ENGINEERING, MECHANICAL
Abdulaziz Alshammari, Rakan C. Chabaan
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引用次数: 0

摘要

摘要异物检测是电动汽车无线充电系统的关键问题之一。本文提出了一种在充电系统中引入感应线圈的异物检测方案。该方法使用多功能隧道电阻传感器矩阵来检测线圈之间是否存在外来金属物体。为了消除盲区,提出了一种非对称感应线圈设计方案。所建议的方法利用尺寸调制的c形线圈单元来去除由磁场轴向均匀性引起的不可见区域。利用ANSYS/MAXWELL软件对传输线圈的感应电压进行了测量。实验结果表明,该方法比常规的均匀感应线圈具有许多优点,包括感应电流的均匀性,位置相关的检测灵敏度和检测精度。它提供了一种可行且经济实惠的方法来克服传统检测线圈的缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electric vehicle wireless charging system for the foreign object detection with the inducted coil with magnetic field variation
Abstract Foreign object detection is one of the most critical issues in electric vehicle wireless charging systems. This article proposes a foreign object detection scheme with an induced coil in the charging system. The proposed method uses a multifunctional tunneling resistance sensor matrix to detect the presence of a foreign metal object between the coils. An asymmetrical induction coil design scheme is proposed to eliminate the blind area. The suggested method utilizes size-modulated c-shaped coil units to remove invisible zones that result from the magnetic field’s axial uniformity. The induced voltage in the transmission coil is measured using ANSYS/MAXWELL software. The experimental results show that the suggested method has a number of benefits over regular even-sensing coils, including higher uniformity in the induced current, position-dependent detection sensitivity, and detection accuracy. It provides a feasible and affordable way to get around the drawbacks of the traditional detecting coil.
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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