Machine Learning–Driven Battery Health Monitoring and Prediction System

Abstract

Battery health prediction plays an important role in today’s energy-based systems such as electric vehicles, renewable storage, and portable devices. This project focuses on developing a machine learning-based system that can classify battery condition as either “Good” or “Warning” using structured data. A web application is designed to make the system easy to use, allowing users to upload datasets, preprocess data, train models, and generate predictions.
The data preprocessing stage includes cleaning, normalization, and splitting to improve model performance. These steps help remove inconsistencies in the data and ensure that the models learn meaningful patterns. Three models—XGBoost, LightGBM, and an Ensemble approach—are evaluated using standard metrics like accuracy, precision, recall, and F1-score. Among them, XGBoost performs the best with perfect results, while the others show slightly lower performance due to limitations in capturing all classification patterns accurately.
The system also supports both bulk and manual predictions, making it practical and flexible for different types of users. It can handle large datasets efficiently and also provide quick predictions for individual inputs. Additionally, the system provides performance comparison results, helping users understand which model works best.
Overall, the project offers a reliable and scalable solution for battery health monitoring. It helps in early detection of battery issues, supports better maintenance planning, and reduces the chances of unexpected failures, thereby improving the efficiency and lifespan of battery-powered systems.