Abstract
The integration of electric vehicles (EVs) into mainstream transportation systems is contingent upon the development of efficient and convenient charging technologies. Wireless charging, in particular, presents a promising solution to address the limitations of traditional plug-in charging methods. However, optimizing wireless charging techniques for EVs remains a complex challenge, with factors such as efficiency, alignment, and safety needing careful consideration. This paper explores the potential of leveraging machine learning (ML) algorithms to enhance the performance of wireless charging systems for EVs. By employing ML techniques, such as neural networks and genetic algorithms, in conjunction with real-time data analysis, the aim is to develop adaptive and intelligent charging systems capable of optimizing various parameters to improve efficiency, reliability, and user experience. This research paper discusses the current state of wireless charging technologies, explores the application of machine learning in optimizing these systems, and presents potential avenues for future research and development
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