Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems
This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10945854/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850152499903725568 |
|---|---|
| author | Federico Amadei Michele Quercio Francesco Riganti Fulginei |
| author_facet | Federico Amadei Michele Quercio Francesco Riganti Fulginei |
| author_sort | Federico Amadei |
| collection | DOAJ |
| description | This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles (EVs), IoT devices, and medical implants, WPT systems face challenges in terms of coil alignment, foreign object detection, and power efficiency. The use of ML algorithms, particularly neural networks and reinforcement learning has emerged as a promising solution to address these challenges. We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. Additionally, ML is being used for foreign object detection (FOD) to ensure safety by identifying metallic and living objects that may interfere with power transmission. The article discusses various approaches, including supervised learning, regression models, and Q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. By reviewing recent advancements and ongoing research, this paper provides a comprehensive overview of the potential and limitations of integrating ML into WPT systems, paving the way for smarter, safer, and more efficient wireless charging technologies. |
| format | Article |
| id | doaj-art-dc89f05959a74bbbb7cef3f4e107c563 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dc89f05959a74bbbb7cef3f4e107c5632025-08-20T02:25:58ZengIEEEIEEE Access2169-35362025-01-0113584435846410.1109/ACCESS.2025.355623510945854Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer SystemsFederico Amadei0https://orcid.org/0009-0005-8847-8862Michele Quercio1https://orcid.org/0000-0001-8086-6979Francesco Riganti Fulginei2https://orcid.org/0000-0001-8824-3776Department of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Rome, ItalyDepartment of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Rome, ItalyDepartment of Industrial, Electronic and Mechanical Engineering, University of Roma Tre, Rome, ItalyThis article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles (EVs), IoT devices, and medical implants, WPT systems face challenges in terms of coil alignment, foreign object detection, and power efficiency. The use of ML algorithms, particularly neural networks and reinforcement learning has emerged as a promising solution to address these challenges. We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. Additionally, ML is being used for foreign object detection (FOD) to ensure safety by identifying metallic and living objects that may interfere with power transmission. The article discusses various approaches, including supervised learning, regression models, and Q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. By reviewing recent advancements and ongoing research, this paper provides a comprehensive overview of the potential and limitations of integrating ML into WPT systems, paving the way for smarter, safer, and more efficient wireless charging technologies.https://ieeexplore.ieee.org/document/10945854/Electric vehicle (EV) chargingforeign object detection (FOD)inductive couplingmachine learning (ML)magnetic field optimizationmisalignment compensation |
| spellingShingle | Federico Amadei Michele Quercio Francesco Riganti Fulginei Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems IEEE Access Electric vehicle (EV) charging foreign object detection (FOD) inductive coupling machine learning (ML) magnetic field optimization misalignment compensation |
| title | Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems |
| title_full | Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems |
| title_fullStr | Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems |
| title_full_unstemmed | Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems |
| title_short | Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems |
| title_sort | recent results on the use of artificial intelligence techniques applied to wireless power transfer systems |
| topic | Electric vehicle (EV) charging foreign object detection (FOD) inductive coupling machine learning (ML) magnetic field optimization misalignment compensation |
| url | https://ieeexplore.ieee.org/document/10945854/ |
| work_keys_str_mv | AT federicoamadei recentresultsontheuseofartificialintelligencetechniquesappliedtowirelesspowertransfersystems AT michelequercio recentresultsontheuseofartificialintelligencetechniquesappliedtowirelesspowertransfersystems AT francescorigantifulginei recentresultsontheuseofartificialintelligencetechniquesappliedtowirelesspowertransfersystems |