Smart Battery Health Monitoring Using Digital Twin and AI/ML Technologies

Adithi M; Damodar G N; Jesmin K Joseph; Nayana S; Shivalingamurthy A G1

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Publication Date: 2024/12/14

Abstract: This project involves integrating a sophisticated battery health monitoring system that leverages Digital Twin (DT) and AIML technologies in conjunction with an Arduino Uno. The system incorporates current, voltage, and temperature sensors to continuously track battery metrics, while employing machine learning algorithms to identify any irregularities. Furthermore, a DC load is utilized to mimic battery usage, and notifications are dispatched through LCD, GSM, and a buzzer. Ultimately, the system guarantees effective battery supervision and timely detection of potential failures.

Keywords: Digital Twin, Artificial Intelligence and Machine learning, GSM Model.

DOI: https://doi.org/10.5281/zenodo.14471042

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24DEC082.pdf

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