Battery Health Prediction and Self Healing Using XGBoost and PWM

Author(s)	Dr. Kokilavani T, Risika M, Varssha K, Ritika G, Priyanka S
Country	India
Abstract	As electric vehicles and renewable energy drive the demand for lithium-ion batteries, predicting their lifespan and maintaining performance have become critical success factors. Our intelligent Battery Management System (BMS) tackles these challenges head-on, using machine learning to monitor and predict battery health with precision. Built around an ATMEGA328P microcontroller and sensors, the system tracks voltage, current, and temperature in real-time, sending data to the cloud via the ESP8266 Wi-Fi module. By applying advanced algorithm like XGBoost, we accurately determine the State of Health (SoH) and predict battery lifespan. The system's innovative self-healing mechanism extends battery life by minimizing wear through Pulse Width Modulation (PWM). Users can access system insights effortlessly through an LCD display and online dashboard. By combining hardware and AI expertise, we've created a reliable, efficient, and sustainable battery management solution that supports the adoption of electric vehicles and renewable energy.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-03

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