proposed a hybrid approach that combines recurrent neural networks (RNNs) with linear regression models for real-time SOC predictions with a flexible number of prediction steps. More recently, transformer-based architectures have been shown to outperform RNN-based models as they capture long-term dependencies of SOC more accurately. Most of the existing literature relies on training models using the timeseries current data from an entire trip for SOC predictions. However, for practical applications in electric vehicles (EVs), it is essential to predict SOC consumption at the beginning or during the early stages of a trip. This allows for timely and informed decision-making based on the initial or partial trip data. Despite the strong forecasting capabilities of modern machine learning models, the challenge of early SOC consumption prediction has not been thoroughly explored in the literature. Therefore, utilizing our multi-fidelity dataset, which includes behavioral and terrain conditions we demonstrate the charge consumption prediction early in the trip. Furthermore, we employ these predicted charge consumption to inform drivers about their usage and offer personalized recommendations for optimizing battery consumption.