Professional Battery RUL Prediction System with Advanced Machine Learning - Predicting Remaining Useful Life (RUL) and State of Performance (SOP) of lithium-ion batteries using LSTM, Transformer, and Ensemble models with 95%+ accuracy. Features real-time analytics dashboard, REST API, and production-ready deployment.

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.

This repository simulates battery aging data and applies machine learning models to classify battery health into aging stages such as Healthy, Moderate Aging, and Aged. The project uses real-world battery data to build predictive models and analyze aging trends for practical applications.

Battery Health Checker

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.