An Intelligent Hybrid Machine Learning Model for Proactive Management of EV Battery Health and Lifecycle

Abstract

The rapid advancement in electric vehicle (EV) technologies necessitates the development of intelligent systems for monitoring battery health and forecasting lifecycle trends to ensure safety, efficiency, and operational longevity. This paper introduces SmartBatt-HML, a predictive health-based intelligent hybrid deep learning framework to be utilized in EV battery predictive health monitoring and optimizing the battery lifecycle. The given architecture is synergetically enhanced with a Convolutional Neural Network (CNN) to extract spatial features and a Bidirectional Gated Recurrent Unit (Bi-GRU) to learn temporal degradation models, attention mechanism to weigh specific cycles selectively, and XGBoost classification model that is more robust to make decisions. The model accepts multi-parameter time-series data on the battery cell voltage, current, temperature, state-of-charge (SOC) and internal resistance, as well as derived electrochemical degradation signatures like incremental capacity (IC) and differential voltage (DVA) scan. The experiments performed on benchmark battery datasets confirm the high performance of the model with classification accuracy, precision, recall, and F1-score equal to 97.19%, 96.85%, 96.92%, and 96.88% respectively. Moreover, SmartBatt-HML accomplishes superbly in Remaining Useful Life (RUL) forecast with Root Mean Square Error (RMSE) score of only 3.74 and Mean Absolute Error (MAE) score of 2.95. Its latency of 19.0 ms also supports the fact that it is ready to be deployed in embedded battery management systems that require real-time inference capability. In this study, the SmartBatt-HML methodology improves both accuracy of prediction and interpretability and scalability to real EV settings. The combination of hybrid architectures and sophisticated feature engineering promises to transform SmartBatt-HML into an excellent tool in the future of battery diagnosis and predictive maintenance policies.