Data processing. An original dataset of 20 velocity drive cycles averaging 585 seconds per trip were selected from an open-source repository. These velocity profiles are time-series velocity data with uniform time intervals between consecutive measurements. Out of these 20 drive cycles, four drive cycles were randomly selected for testing while the remaining 16 drive cycles were used for training ML models. Additionally, three reference drive cycles were derived from these 20 drive cycles to establish baseline charge consumption. These reference cycles represent (i) a trip at constant maximum velocity (33.89 m/s), (ii) a trip at constant mean velocity (19.4 m/s), and (iii) a trip featuring a single braking event between maximum to mean velocity. Since all velocity drive cycles were recorded for trips already in motion, a 300-second interval was added to the beginning of each cycle to create a more realistic representation of an EV trip starting from rest. During this period, the velocity gradually increased from 0 m/s to match the velocity at the first recorded data point. This initial acceleration phase was generated using an interpolation method and ensured a smooth and realistic transition to the original velocity values.