This study presents a comprehensive dataset of 117 profiles, encompassing velocity and current data from diverse terrain, driving, and charging patterns. The addition of these factors significantly enhanced prediction accuracy by reducing MAPE values from 7.3% to 5.4% for velocity models. For current models, the improvement was less pronounced with MAPE decreasing from 5.1% to 4.3%. This is attributed to the more sophisticated nature of current profile data, which already includes embedded terrain, driving, and charging information. Additionally, the prediction performance at two different time intervals showed similar MAPE values with current model 2 showing best results of 4.3% (early prediction 1) and 2.7% (early prediction 2). These early predictions are valuable for drivers seeking to optimize their trips through route modifications, driving style adjustments, or strategic charging. By providing researchers with a more field-relevant dataset, this work aims to facilitate the development of advanced ML models capable of accurate and timely SOC predictions, ultimately alleviating range anxiety in EV drivers.