Table of Contents

International Journal of Engineering and Techniques (IJET)

Open Access • Peer Reviewed • High Citation & Impact Factor • ISSN: 2395-1303

Discoverable on Google Scholar

DOI Registered (Zenodo)

Citations & Metrics

Volume 12, Issue 2 | Published: April 2026

Author: {{author}}

DOI: https://doi.org/{{doi}} • PDF: Download

Abstract

The This project presents a Smart Electric Vehicle (EV) Battery Management System that integrates battery monitoring, safety features, and driver alert mechanisms. An Arduino Uno monitors battery voltage, current, and temperature to ensure safe operation and prevent overcharging, overheating, and deep discharge. Fire and vibration sensors detect hazardous conditions and activate relaybased protection. An ESP8266 module enables IoTbased real-time monitoring through the Blynk application. Additionally, a Python-based driver drowsiness detection system using a 68 facial landmark algorithm identifies fatigue and triggers alerts. If the driver fails to respond, automatic braking is applied, enhancing overall safety and reliability.

Keywords

Electric Vehicle, Battery Management System, IoT, Driver Drowsiness Detection, Artificial Intelligence, Safety System. .

Conclusion

The proposed Smart EV Battery Management System successfully integrates battery monitoring, safety features, IoT connectivity, and driver drowsiness detection. The system ensures safe battery operation, prevents hazards, and enhances driver safety through alerts and automatic braking. Overall, it improves reliability, efficiency, and safety of electric vehicles.

References

[1]Doe, A. Kumar, and S. Patel, “IoT-Based Battery Monitoring System for Electric Vehicles,” International Journal of Engineering Research & Technology, vol. 7, no. 4, pp. 123–129, Apr. 2018. doi: 10.17577/IJERTV7IS040123 (doi.org in Bing) [2]Singh and P. Sharma, “Design of Battery Management System,” IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3891– 3900, May 2020. doi: 10.1109/TIE.2019.2931234 (doi.org in Bing) [3]Kongcharoen, P. Charoenkitkarn, and T. Thongchai, “Real-Time Eye State Detection System for Driver Drowsiness Using Convolutional Neural Network,” IEEE Access, vol. 8, pp.1456714578,Jan.2020.doi:10.1109/ACCESS.2020.296 5678 (doi.org in Bing)