Automatic Vehicle Cabin Air Recirculation using Raspberry Pi

References

[1] Regi Fernando, Suharjito, “In-Car Air Quality Notification Using Internet of Things Platform”, May 2023, https://www.researchgate.net/publication/371676232

[2] Falah Y H Ahmed , Jabar H. Yousif , Marwan Alshar’e , Maram El Sheikh , Ehsan Al-Ajmi , Mahmood Al-Bahri, Smart “In-Cabin Air Monitoring System using IoT Technologies”, January 2024, https://www.researchgate.net/publication/377786006

[3] C. Balasubramaniyan* and D. Manivannan, “IoT Enabled Air Quality Monitoring System (AQMS) using Raspberry Pi”, October 2016, https://www.researchgate.net/publication/309713172

[4] Michael L. Grady and Heejung Jung, Univ of California-Riverside, “Vehicle Cabin Air Quality with Fractional Air Recirculation”, April 2013, https://www.researchgate.net/publication/289423736

[5] Rui Zhang , Minglu Zhao , Hengwei Wang , Haimei Wanga , Hui Konga , Keliang Wang , Petros Koutrakisc , Shaodan Huang and Jianyin Xiong, “Cabin air dynamics: Unraveling the patterns and drivers of volatile organic compound distribution in vehicles”, July 2024, https://doi.org/10.1093/pnasnexus/pgae243

[6] Somansh Kumar, Ashish Jasuja, “Air Quality Monitoring System Based on IoT using Raspberry Pi”, May 2017, https://www.researchgate.net/publication/322001313

[7] Abdul Syafiq Abdull Sukor , Goh Chew Cheik , Latifah Munirah Kamarudin , Xiaoyang Mao ,Hiromitsu Nishizaki, Ammar Zakaria and Syed Muhammad Mamduh Syed Zakaria, “Predictive Analysis of In-Vehicle Air Quality Monitoring System Using Deep Learning Technique”, September 2022, https://www.mdpi.com/1856166

[8] Heejung S. Junga,b,* , Michael L. Gradya,b, Tristan Victoroffc , and Arthur L. Millerc, “Simultaneously reducing CO2 and particulate exposures via fractional recirculation of vehicle cabin air”, July 2017, https://doi.org/10.1016/j.atmosenv.2017.04.014

[9] Fausto Arpino, Giorgio Grossi, Gino Cortellessa , Alex Mikszewski , Lidia Morawska , Giorgio Buonanno, Luca Stabile, “Risk of SARS-CoV-2 in a car cabin assessed through 3D CFD simulations”, March 2022, https://doi.org/10.1111/ina.13012

@article{1708223,
      author = {Mohamed Thoufeeq J , Lavanyaa K R, Prasanna Kumar V , Abinaya S, Vinotheni M S},
      title = {Automatic Vehicle Cabin Air Recirculation using Raspberry Pi},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {95-100},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708223.pdf},
      abstract = {Existing air circulation systems of vehicles mostly depend on manual controls, which have not proved effective in responding to changing air quality conditions. This can result in Poor ventilation, high CO? levels, and higher risk of health complications. This research consists of the design with the development of a smart air quality monitoring and re-circulation system for vehicles with the help of python software. The system is having sensors integrated to monitor carbon dioxide (CO?) in real-time. Automated actions are to be applied for adjustment of cabin ventilation. This study presents a CO? monitoring system using an MQ135 sensor and Raspberry Pi 4. The ADS1115 ADC converts sensor data, which is analyzed via Python on Raspbian OS. When CO? exceeds 1000 ppm, the system triggers an optocoupler relay to activate an LED, simulating an air recirculation control. This ensures real-time air quality monitoring for improved passenger safety. The results from experiments show remarkable improvements and management of air quality, thus giving a smart cost-effective and efficient safety measure.},
      keywords = {Air Quality, CO2, Raspberry Pi, Recirculation.},
      month = {May},
  }