Version 1
: Received: 8 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (06:57:35 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.v1
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.v1
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.v1
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.v1
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.v1
Abstract
The integration of Wireless Power Transfer (WPT) technologies with Artificial Intelligence (AI) is poised to revolutionize multiple industries, including consumer electronics and industrial applications. This study delves into the latest advancements in WPT, with a focus on AI-enhanced efficiency, range, and reliability. AI-driven algorithms optimize power transfer, improve device alignment, and predict maintenance needs, leading to smarter and more sustainable power solutions. Machine learning and computer vision are employed to achieve precise alignment, while predictive maintenance minimizes downtime and costs. Additionally, AI-powered power management systems adapt to varying demands, optimizing energy usage. This research highlights the transformative potential of AI in enhancing WPT technologies, with significant implications for next-generation applications. The findings emphasize the importance of ongoing research in this interdisciplinary field to address existing challenges and unlock new opportunities for innovation.
Keywords
Wireless Power Transfer (WPT); Artificial Intelligence (AI); Machine Learning; Predictive Maintenance; Energy Harvesting; Dynamic Alignment
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.
