We evaluate the significance of behavioral and trip-specific features in our novel dataset by modeling the charge usage score and comparing its predictive performance against models trained on the original velocity drive cycles. From each dataset, we selected four input features listed in Table and charge usage score, which directly represents the final SOC drop for a given trip, was set as the target variable for both datasets. Two types of models were developed based on the training data, a) velocity-based models and b) current-based models. In addition to these baseline models (model 1), we introduced a second variant (model 2) that incorporates features related to terrain, charging behavior, and driving patterns. These features are listed in Table , were incrementally integrated into current profiles but are absent in the velocity-based dataset. Therefore, model 2 was introduced to assess the impact of these additional features on predictive performance. Furthermore, features representing cell-to-cell variations were included in Model 2 (see Methods). The results for model 2 are shown in Figures and. Both models demonstrated improved performance compared to their model 1 counterparts after the incorporation of additional features. Notably, velocity model 2 exhibited a significant reduction in MAPE from 7.5% to 5.3%, while current model 2 showed a more modest improvement, reducing MAPE from 5.2% to 4.3%. The smaller performance gain of 0.9% in current model 2 is because behavioral and terrain features were already present, so their inclusion had a limited impact. In contrast, adding these features to velocity model 2 provided more substantial trip-related information and reducing the MAPE by 2.2%.