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

Abstract

Electric Vehicles (EVs) are rapidly transforming the global transportation ecosystem due to their environmental advantages, reduced greenhouse gas emissions, and decreased dependence on fossil fuels. As EV adoption increases, efficient battery management has become a critical challenge, particularly in battery swapping systems where batteries are frequently exchanged and subjected to varying operational conditions. In such systems, accurate estimation of battery health is essential to ensure operational safety, reliability, and cost-effectiveness.

 This paper presents a machine learning-driven framework for real-time estimation of key battery health parameters, including State of Health (SoH) and Remaining Useful Life (RUL). The proposed system leverages ensemble learning techniques, specifically Random Forest Regression and XGBoost, to analyze battery operational data such as voltage, current, temperature, and cycle count. These models are capable of capturing complex nonlinear relationships among battery parameters and provide accurate predictive insights. Furthermore, the trained models are integrated into a Flask-based web application, enabling real-time predictions and user-friendly interaction.

 Experimental results demonstrate that the proposed system outperforms traditional rule-based and threshold-based approaches in terms of accuracy, robustness, and computational efficiency. The system achieves prediction accuracy exceeding 95% while maintaining low error rates and fast response times. The proposed solution facilitates predictive maintenance, reduces the risk of battery degradation, enhances safety, and improves the overall efficiency of EV battery swapping stations.