Version 1
: Received: 8 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (06:57:35 CEST)
Version 2
: Received: 5 August 2024 / Approved: 6 August 2024 / Online: 6 August 2024 (06:19:48 CEST)
How to cite:
ISLAM, M. S. Advancements in Wireless Power Transfer (WPT) Technologies Enhanced by AI for Next-Generation Applications. Preprints2024, 2024060663. https://doi.org/10.20944/preprints202406.0663.v2
ISLAM, M. S. Advancements in Wireless Power Transfer (WPT) Technologies Enhanced by AI for Next-Generation Applications. Preprints 2024, 2024060663. https://doi.org/10.20944/preprints202406.0663.v2
ISLAM, M. S. Advancements in Wireless Power Transfer (WPT) Technologies Enhanced by AI for Next-Generation Applications. Preprints2024, 2024060663. https://doi.org/10.20944/preprints202406.0663.v2
APA Style
ISLAM, M. S. (2024). Advancements in Wireless Power Transfer (WPT) Technologies Enhanced by AI for Next-Generation Applications. Preprints. https://doi.org/10.20944/preprints202406.0663.v2
Chicago/Turabian Style
ISLAM, M. S. 2024 "Advancements in Wireless Power Transfer (WPT) Technologies Enhanced by AI for Next-Generation Applications" Preprints. https://doi.org/10.20944/preprints202406.0663.v2
Abstract
The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.
Keywords
Wireless Power Transfer; Artificial Intelligence; machine learning; predictive maintenance; energy optimization
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
