Authors : Shasikala G.; Nayana N.; Thameem N.; Sonali P.

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/4mybxv9d

DOI : https://doi.org/10.38124/ijisrt/25may778

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project presents the development of an IoT-based Battery Management System (BMS) utilizing the Random Forest Regressor machine learning model for remote battery health monitoring and optimization. The system integrates IoT-enabled sensors to collect real-time battery parameters such as voltage, current, and temperature. This data is transmitted through secure IoT gateways to a cloud platform for processing. The Random Forest Regressor is employed to predict critical battery metrics, including capacity degradation and remaining useful life. The system enhances predictive accuracy and enables informed decision-making for optimized battery usage, thereby improving efficiency and longevity. This innovative solution demonstrates the potential of combining IoT and machine learning to revolutionize battery management and foster sustainable energy solutions.

Keywords : IoT, Battery Management System, Random Forest Regressor, Remote Monitoring, Machine Learning, Battery Optimization, Predictive Analytics, Sustainable Energy.

References :

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