| Title |
A Study on Localization of EV Charging Port based on Image Processing for Autonomous Charging |
| Authors |
남세현(Se Hyun Nam) ; 박종우(Jongwoo Park) ; 한병길(Byoungkil Han) ; 이영훈(Young Hun Lee) ; 도현민(Hyunmin Do) |
| DOI |
https://doi.org/10.5573/ieie.2025.62.6.53 |
| Keywords |
Image processing; Localization; Artificial intelligence; Deep learning; Electric vehicle |
| Abstract |
With the recent expansion of electric vehicle (EV) adoption and the growth of related industries, the importance of charging port localization technology has increased significantly. This technology has become essential in various fields, including automated charging systems, charging infrastructure design, and integration with autonomous vehicles. Unlike internal combustion engine vehicles, EVs rely on batteries as their primary power source, requiring frequent and lengthy charging sessions and necessitating precise alignment of the charging port. To address these challenges, accurate localization and recognition of the charging port's position and shape are essential. Although AI-based approaches have shown high applicability and performance in recent research, their reliance on large training datasets and high computational costs pose limitations for real-world system implementation. Furthermore, it remains unclear whether these methods guarantee better performance than traditional image-processing-based methods in real-world environments. In this study, we propose an image-processing-based method for electric vehicle charging port localization and compare its performance with that of representative AI-based techniques, such as deep learning, to analyze its effectiveness. By designing a system capable of reliably recognizing charging ports in diverse environments, this research aims to enhance the efficiency of EV charging processes in robotic systems and contribute to the advancement of automated charging technologies. |