IRO Journals
Journal of Electrical Engineering and Automation

IoT-Enabled Edge–Cloud Battery Management System for Real-Time Monitoring

Open Access
Published: 30 June, 2026
Pages: 168-180
  • Siva Sankar R. , Siva Sankar R.
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology, Kangayam, India
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology, Kangayam, India
  • Gokila Nivetha E. Gokila Nivetha E.
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology, Kangayam, India
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology, Kangayam, India
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How to Cite

R., Siva Sankar, and Gokila Nivetha E. 2026. “IoT-Enabled Edge–Cloud Battery Management System for Real-Time Monitoring”. Journal of Electrical Engineering and Automation 8 (3): 168-80. https://doi.org/10.36548/jeea.2026.3.001.
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Keywords

Battery Management System (BMS)
Internet of Things (IoT)
Edge-Cloud
Raspberry Pi Pico (RP2040)
ESP8266 Wi-Fi module
lightweight Decision Tree

Abstract

The growing usage of lithium-ion batteries in electric vehicles, renewable energy generation and consumption systems, and portable electronic devices increases the need for a smart battery monitoring and management solution. In this paper, an IoT-enabled Edge–Cloud Battery Management System (BMS) is proposed to provide real-time monitoring and predictive analysis by means of lightweight Decision Tree classifier. The suggested approach is based on a unification of battery sensing, edge computing, wireless communication, and cloud analytics processes. Voltage, current, and temperature of a battery are monitored continuously using a voltage divider circuit, ACS712 current sensor, and DS18B20 temperature sensor. Data processing, battery state classification, and activation of battery protection mechanisms under abnormal conditions are done by Raspberry Pi Pico (RP2040) device, and Wi-Fi connection via ESP8266 module is used to communicate with a cloud platform. Battery state classification is performed into such three states as Normal, Warning, and Critical depending on voltage, current, and temperature values and protection is provided when abnormal state of the battery is detected. The results of experimental evaluation of the system demonstrate average Decision Tree execution time of 12 ms, end-to-end monitoring latency of 163 ms, packet delivery ratio of 98.2%, and cloud upload success rate of 98.7%.

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