A Dual-Side Magnetic Integration–Based Receiver Detection Method of Long-Track DWPT System

Wang, Junhua; Wan, Leke; Cai, Changsong; Xue, Ming; Du, Yinfeng; Zhang, Jin

doi:10.1109/tpel.2024.3374825

Cited by 1 publication

(1 citation statement)

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“…In [10], an innovative receiver position identification system was employed, which utilized a ferrite core counter system with a Ferrite Position Identification (FPID) group messaging unit, to determine the receiver's position through the counting of strategically placed ferrite cores within the DWC system. The authors of [14] proposed a dual-side magnetic integration of the inductor of the LCC-LCC compensation network to detect the vehicle position by utilizing a solenoidintegrated coil at the transmitter and a bipolar-integrated coil at the receiver, thereby eliminating the need for extra sensors. However, incorporating additional detection coils in the aforementioned approaches presents a challenge, as it causes an increase in the volume and cost of the DWC system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Behnamfar,

Stevenson,

Tariq

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%