๐ EV battery health monitored with digital twin tech
Researchers introduced a Battery Health State Index-driven digital twin framework for EV battery condition assessment. This framework was developed using laboratory cycling data from an Arbin system. The study utilized Random Forest, achieving an Rยฒ of 0.998, and integrated this with charging data to create a Digital Twin Score (DTS). This DTS effectively categorized charging sessions into low, medium, and high-risk levels. The system offers an interpretable solution for intelligent maintenance support in EV charging environments. ๐