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

Electric vehicle (EV) battery swapping systems require accurate battery condition assessment to ensure safe reuse and efficient energy management. Battery degradation over time creates major operational challenges for swapping stations, affecting reliability, lifecycle management, and resource utilization. Traditional monitoring techniques often rely on fixed thresholds or manual inspection, which are not effective in identifying complex degradation patterns in lithium-ion batteries. This research proposes a machine learning-based approach for real-time battery health estimation using ensemble learning algorithms. The system employs Random Forest Regression and Extreme Gradient Boosting (XGBoost) models to analyze historical battery data and predict key health indicators such as State of Health (SoH) and remaining charge cycles. Various battery parameters including voltage, current, temperature, and usage cycles are used as input features to identify patterns associated with battery performance and degradation. Data preprocessing techniques such as normalization, handling missing values, and feature selection are applied to improve model accuracy and reliability. Hyperparameter tuning is performed to optimize model performance and enhance prediction capability. Experimental results demonstrate that the proposed system achieves high prediction accuracy and enables battery swapping station operators to make informed decisions regarding battery reuse, maintenance scheduling, and replacement strategies. The system ultimately contributes to improved battery lifecycle management, enhanced operational efficiency, and sustainable electric vehicle infrastructure.