Explainable Data Driven Digital Twins for Predicting Battery States in Electric Vehicles

This research introduces an innovative approach to predicting battery states for electric vehicles (EVs) using an Explainable Data-Driven Digital Twin framework. As EV adoption grows, optimizing battery performance becomes critical for ensuring vehicle reliability and efficiency. The framework focuses on predicting two essential metrics—SOC and SOH— models for machine learning like SVM, SVR, RF, etc.