Sensorless Vehicle Detection Using Vehicle Side Voltage Pulses for In-motion WPT

Shirasaki, Daisuke; Fujimoto, Hiroshi; Hori, Yoichi

doi:10.1109/wow47795.2020.9291291

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Research about Rx-side sensorless WPT systems has been undertaken in [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The study in [ 15 ] proposes a machine learning (ML) assisted method that estimates the power delivered to the Rx by using only measurements at the Tx-side.…”

Section: Introductionmentioning

confidence: 99%

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

Kim,

Niada,

Park

2024

Sensors

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Improvement of wireless power transfer (WPT) systems is necessary to tackle issues of power transfer efficiency, high costs due to sensor and communication requirements between the transmitter (Tx) and receiver (Rx), and maintenance problems. Analytical techniques and hardware-based synchronization research for Rx-sensorless WPT may not always have been available or accurate. To address these limitations, researchers have recently employed machine learning (ML) to improve efficiency and accuracy. The objective of this work was to replace Tx–Rx communication with ML, utilizing Tx-side parameters to predict the load and coupling coefficients on an LC–LC tuned WPT system. Based on current and voltage features collected on the Tx-side for various load and coupling coefficient values, we developed two models for each load and coupling prediction. This study demonstrated that the extra trees regressor effectively predicted the characteristics of LC–LC tuned WPT systems, with coefficients of determination of 0.967 and 0.996 for load and coupling, respectively. Additionally, the mean absolute percentage errors were 0.11% and 0.017%.

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Section: Introductionmentioning

confidence: 99%

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

Kim,

Niada,

Park

2024

Sensors

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Novel Coil Detection Method for Dynamic Wireless Power Transfer for Electric Vehicles

Huang¹,

2023

Int. J. ITS Res.

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Design of Automatic Switch of Charging Rail for Electric Vehicle Dynamic Charging System Based on Wireless Power Transmission

Pang

2021

2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA)

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Sensorless Vehicle Detection Using Vehicle Side Voltage Pulses for In-motion WPT

Cited by 6 publications

References 9 publications

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning

Novel Coil Detection Method for Dynamic Wireless Power Transfer for Electric Vehicles

Design of Automatic Switch of Charging Rail for Electric Vehicle Dynamic Charging System Based on Wireless Power Transmission

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