Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/2/501 |
| _version_ | 1797339432835088384 |
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| author | Minhyuk Kim Wend Yam Ella Flore Niada Sangwook Park |
| author_facet | Minhyuk Kim Wend Yam Ella Flore Niada Sangwook Park |
| author_sort | Minhyuk Kim |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T09:46:52Z |
| format | Article |
| id | doaj.art-b63db234cd284e62b3544ae9abce052a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:52Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b63db234cd284e62b3544ae9abce052a2024-01-29T14:15:33ZengMDPI AGSensors1424-82202024-01-0124250110.3390/s24020501Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine LearningMinhyuk Kim0Wend Yam Ella Flore Niada1Sangwook Park2EM Environment R&D Department, Korea Automotive Technology Institute, Cheonan 31214, Republic of KoreaDepartment of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of KoreaDepartment of Electronic Engineering, Soonchunhyang University, Asan 31538, Republic of KoreaImprovement 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%.https://www.mdpi.com/1424-8220/24/2/501wireless power transferimpedance matchingmachine learningload resistance estimationcoupling coefficient estimation |
| spellingShingle | Minhyuk Kim Wend Yam Ella Flore Niada Sangwook Park Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning Sensors wireless power transfer impedance matching machine learning load resistance estimation coupling coefficient estimation |
| title | Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning |
| title_full | Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning |
| title_fullStr | Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning |
| title_full_unstemmed | Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning |
| title_short | Predicting Receiver Characteristics without Sensors in an LC–LC Tuned Wireless Power Transfer System Using Machine Learning |
| title_sort | predicting receiver characteristics without sensors in an lc lc tuned wireless power transfer system using machine learning |
| topic | wireless power transfer impedance matching machine learning load resistance estimation coupling coefficient estimation |
| url | https://www.mdpi.com/1424-8220/24/2/501 |
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