Solar radiation monitoring system for electric vehicle charging using solar modules based on the Internet of Things

Authors

  • Yusuf Dewantoro Herlambang Politeknik Negeri Semarang Author
  • Muhamad Nurul Huda Politeknik Negeri Semarang Author
  • Daffa Yudha Akbar Putra Antoro Politeknik Negeri Semarang Author
  • Hanif Faizal Ghozali Politeknik Negeri Semarang Author
  • Martando Robby Setyawan Politeknik Negeri Semarang Author
  • Nanang Apriandi Apriandi Politeknik Negeri Semarang Author
  • Avicenna An-Nizhami Politeknik Negeri Semarang Author
  • Padang Yanuar Politeknik Negeri Semarang Author
  • Elfrida Rizky Riadini Politeknik Negeri Semarang Author
  • Marliyati Marliyati Politeknik Negeri Semarang Author

Keywords:

Electric Vehicles, Internet of Things, Real-time Monitoring, Solar Energy, Solar Radiation

Abstract

The utilization of solar energy to support electric vehicle charging still faces challenges related to efficiency and real-time solar radiation monitoring. Addressing this issue is critical given the importance of optimizing renewable energy to facilitate the transition toward sustainable transportation. This study offers a solution through the development of an Internet of Things (IoT)--based solar radiation monitoring system capable of measuring solar light intensity in real time. The system employs a BH1750 sensor integrated with an ESP32 microcontroller to process data, transmit it to the Firebase Realtime Database, and display it via an Android application. The methodology encompasses the design, implementation, and testing of the system on an electric vehicle placed in an open area for 10 hours of observation. Results indicate that the highest light intensity, recorded at 98,321 lux, corresponded to solar radiation of 776.74 W/m², while the lowest light intensity, 69 lux, resulted in radiation of 0.55 W/m². The implications of this research include enhanced efficiency in electric vehicle charging, the advancement of IoT-based solar energy systems, and the potential integration with energy storage technologies and predictive algorithms to improve energy sustainability.

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Published

2025-03-31

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Section

Articles