The project analyzes battery cycling data to predict degradation patterns and performance metrics using both deep learning (LSTM) and traditional machine learning (XGBoost) approaches. The implementation enables accurate estimation of battery health, which is crucial for battery management systems in various applications.

BattSense is a machine learning project focused on predicting the State of Health (SOH) of lithium-ion batteries using operational parameters such as voltage, current, temperature, and capacity. The model enables accurate, data-driven diagnostics for battery performance monitoring in electric vehicles and portable devices.