We also develop a quantitative metric, the charge saving score, to assess the battery consumption savings of a trip relative to a same-duration trip at constant maximum velocity. By incorporating route and human behavior data, our research compares the improvement in prediction accuracy of the charge saving score for ML models developed using velocity and current data. Additionally, we demonstrate the effectiveness of early predictions of charge saving score, using only the first 300-350 seconds of trip data to provide drivers with personalized recommendations. These early insights allow drivers to plan their routes, adjust their driving styles, or optimize charging strategies, ultimately reducing EV range anxiety.