Machine Learning Assisted Characteristics Prediction for Wireless Power Transfer Systems
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
40496-40505
40496-40505
Series
IEEE Access, Volume 10
Abstract
One of the main challenges in wireless power transfer (WPT) devices is performance degradation when the receiver's position and characteristics vary. Therefore, the load resistance and receiver position must be monitored to ensure proper optimization of power transfer. This study proposes a machine learning (ML) assisted method that estimates the power delivered to the receiver by only using measurements at the transmitter side. Based on the delivered power estimation, we also propose a method to identify if the system efficiency is too low, so that the transmitter should be turned off. This activation control method can be useful in multi-transmitter WPT systems. In addition, we propose an ML method to estimate the load resistance and the coupling coefficient. Using the proposed method, the characteristics of an inductor-capacitor-capacitor (LCC)-Series tuned WPT system are successfully predicted only using the measured root-mean-square and the harmonic contents of the input current. The proposed approach is experimentally validated using a laboratory prototype.Description
Funding Information: This work was supported in part by the Walter Ahlstromin Saatio under Grant 20220056; and in part by the Academy of Finland Postdoctoral Researcher under Grant 333479; and in part by the Business Finland Research-to-business under Grant 1527/31/2020
Keywords
coupling strength estimation, load resistance estimation, machine learning, Wireless power transfer
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Citation
Mahmud, S A A, Jayathurathnage, P & Tretyakov, S A 2022, ' Machine Learning Assisted Characteristics Prediction for Wireless Power Transfer Systems ', IEEE Access, vol. 10, pp. 40496-40505 . https://doi.org/10.1109/ACCESS.2022.3167162
