In recent years, machine learning (ML) algorithms such as regression, support vector machines, and neural networks have been explored for state-of-charge (SOC) estimations due to their predictive capabilities with large datasets, without requiring prior knowledge of battery physics. However, the prediction accuracy of these models is highly dependent on the quality of the training data. Most current ML models are trained on laboratory data, which often exhibits a significant gap when compared to field data, failing to account for diverse driving behaviors. Given that each trip is unique, with individuals displaying varying driving and charging patterns, the battery SOC can also fluctuate accordingly. Therefore, a comprehensive dataset that closely reflects practical EV operation by including terrain and driver behaviors is essential for accurate SOC predictions, which will ultimately help reduce range anxiety among EV drivers. In this study, we present the first comparative dataset consisting of 117 profiles of velocity and current data, representing a diverse range of route profiles, driving styles, and charging patterns. As illustrated in Figure , we begin by utilizing an open-source velocity profile data, which are simulated using an EV Simulink model to derive the corresponding current demand of an EV. Using these data, three cylindrical cells are tested in battery cycler to create the final dataset. The resulting velocity and current profiles are subsequently used to predict the charge consumption of EV.