To address the need for a comprehensive dataset and facilitate early prediction and recommendation to drivers, we present three key strategies aimed to reduce EV range anxiety as shown in Figure . For the first time, we introduce a multifidelity dataset of 117 battery performance profiles (current profiles) derived from 23 velocity drive cycles, capturing a wide range of terrain conditions and variations in human driving behaviors and charging patterns. We obtain these velocity data from open-source datasets and simulate them using our in-house EV Simulink model. This model outputs time-series current data that represent the charge consumption of an EV across different scenarios. To further validate these current profiles, we conduct experimental tests on three 21700 cells under different charging policies that mimic real-world charging habits, resulting in a comprehensive dataset of current profiles. We then train existing ML models on battery cycler-generated current profiles and initial velocity drive cycles to assess their predictive performance. Our results show that explicitly incorporating trip-specific features in both datasets significantly improves model accuracy. To quantify battery consumption, we introduce a novel metric, charge usage score, which compares charge consumption relative to a same-duration trip at constant maximum velocity. Finally, leveraging the predictive performance of our models, we show that charge consumption for a trip can be accurately estimated using only the first 10% of trip data. Early SOC predictions will allow drivers to adjust their routes, driving styles and charging strategies in real time. These reliable, personalized insights will provide drivers more confidence in the estimated charge usage of their EVs and reduce range anxiety.