Machine Learning-Driven Real-Time Battery Health Estimation for EV Battery Swapping

Abstract

Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and reliable battery management systems, especially in battery swapping infrastructures. This project presents a Machine Learning-driven web application for real-time battery health estimation, aimed at enhancing the efficiency of EV battery swapping systems. Built using Flask as the backend web framework and Python for data processing and machine learning, the system predicts two critical parameters of battery condition: State of Health (SoH) and remaining charge cycles. To achieve accurate predictions, the application leverages Random Forest Regression and XGBoost, two powerful ensemble learning algorithms, trained on historical battery usage data including charge/discharge current, voltage, temperature, and cycle counts. The system processes user input in real time and displays the battery’s health status via a user-friendly interface, enabling swift decision-making at battery swapping stations. This solution not only promotes proactive maintenance and optimal utilization of EV batteries but also supports sustainable energy practices by reducing the chances of premature battery disposal. The combination of ML with a lightweight Flask-based deployment makes the application scalable, efficient, and suitable for integration into real-world EV infrastructure. Keywords— Electric Vehicle, Battery Swapping, Battery Health, State of Health, Remaining Useful Life, XGBoost, Random Forest, Flask.