In this paper, we developed a multi-fidelity dataset designed to replicate real-world EV battery performance by mapping 23 velocity drive cycles to 117 current profiles while incorporating trip-specific information about terrain and human behavior. These features were added progressively, beginning with the classification of driving styles from the original velocity profiles. The classified profiles were then simulated using EV Simulink model under varying terrain conditions. The resulting simulated drive cycles were subsequently employed to generate current profiles that capture realistic charging behaviors. These trip-specific features were represented using a one-hot encoding scheme to facilitate easy transferability and ensure consistency between the velocity and current datasets. This approach helps to preserve a uniform structure for the categorical data and enhances the interpretability of the model. Previous research has shown that by explicitly encoding categorical features can improve both interpretability and predictive performance . By integrating both route characteristics and driver behavior, this dataset serves as a comprehensive resource for investigating the factors influencing battery consumption across diverse real-world driving conditions.