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Abstract：
Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle\'s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.
摘要：
动态无线充电（DWC）已成为一种可行的方法，可以通过确保对行驶中的电动汽车进行连续和不间断的充电来减轻范围焦虑。DWC系统依赖于发射器的长度，可以分为长轨道发射机和分段线圈阵列。分段线圈阵列，因其提高效率和减少电磁干扰而受到青睐，成为首选。然而,在这样的DWC系统中，需要检测车辆的位置，特别地,以激活与接收器垫对准的发射器线圈并且去激励未耦合的发射器线圈。本文介绍了用于精确确定车辆位置的各种机器学习算法，适应不同的离地间隙的电动汽车和各种速度。通过测试八种不同的机器学习算法并比较结果,随机森林算法脱颖而出，显示预测实际位置的最低误差。
