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International Journal of Science, Strategic Management and Technology

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ISSN: 3108-1762 (Online)
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IOT BASED ELECTRICAL VEHICLE BATTERY MANAGEMENT SYSTEM WITH CHARGE MONITOR AND FIRE PROTECTION

AUTHORS:
B. Tulasi
M. Mallikarjunareddy
K.Lavanya
D. Manoj kumar
M. Rohit kumar
Mentor
E. Varenya
Affiliation
Dept. of EEE, Sir C R Reddy College of Engineering, Eluru, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

The implementation of this technology would enhance the performance and improve efficiency and safety associated with electric vehicle operation through a battery management system. This prototype is controlled by an Arduino circuit that is used in capturing information about the battery parameters including voltage, current, and temperature. Real-time information concerning the battery parameters is displayed on the I2C LCD. Indicators are used for showing the state of the batteries. In case of any changes in the temperature, the information will be sent to a NodeMCU board, which in turn sends the information to the ThingSpeak IoT platform. Fire safety is enhanced in this prototype through detecting any abnormal temperature and activating the relays to either cool down the battery using a fan or isolate the battery from other circuits. The user can use switches and potentiometer to set the parameters. A DC motor is used to simulate the movement of the car using the battery system.

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Tulasi, B., Mallikarjunareddy, M., K.Lavanya, , kumar, D. M. & kumar, M. R. (2026). IOT Based Electrical Vehicle Battery Management System with Charge Monitor and Fire Protection. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.485

Tulasi, B., et al.. "IOT Based Electrical Vehicle Battery Management System with Charge Monitor and Fire Protection." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.485.

Tulasi, B.,M. Mallikarjunareddy, K.Lavanya,D. kumar, and M. kumar. "IOT Based Electrical Vehicle Battery Management System with Charge Monitor and Fire Protection." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.485.

References
1.Kumar, R. Singh, and P. Verma (2024), “IoT-Based Electric Vehicle Battery Monitoring and Management System,” International Journal of Advanced Research in Electrical Engineering, vol. 15, no. 3, 45–52.

2.Reddy and M. Karthik (2023), “Smart Battery Management System for Electric Vehicles Using IoT,”

3.International Journal of Engineering Research & Technology (IJERT), vol. 12, no. 5, pp. 2100–2106.

4.Sharma, D. Gupta, and A. Mehta (2022), “Real-Time EV Battery Monitoring Using Cloud and IoT,”

5.IEEE Access, vol. 10, pp. 55678–55687.

6.N. Rao and V. Tejaswini (2023), “Design of IoT-Based Battery Health Monitoring System for Electric Vehicles,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 7, no. 8, pp. 1–6.

7.Ali, S. Khan, and T. Hussain (2022), “Wireless Battery Management System for Electric Vehicles,” in

8.Proc. IEEE Int. Conf. on Smart Energy Systems, pp. 120–125.

9.Mishra and P. Das (2021), “IoT-Based Energy Monitoring System for Electric Vehicles,” in Proc. IEEE Int. Conf. on Internet of Things (iThings), pp. 310–315.

10.Lee, H. Park, and B. Kim (2023), “AI-Based Battery State Prediction for Electric Vehicles,” IEEE Transactions on Industrial Electronics, vol. 70, no. 4, pp. 3500–3508.

  1. Patel and N. Shah (2022), “Cloud-Integrated Smart Battery Monitoring System Using ESP32,” International Journal of Modernization in Engineering Technology and Science (IJMETS), vol. 4, no. 6, pp. 1000–1005.

  2. Nguyen, P. Tran, and H. Hoang (2023), “Real-Time Fault Detection in EV Batteries Using Embedded IoT Devices,” IEEE Sensors Journal, vol. 23, no. 7, pp. 7654–7662.

  3. Johnson and R. Williams (2023), “Deep Learning-Based Battery Health Estimation for Electric Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, pp. 1023–1032.

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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