Battery electric vehicle (BEV) routing in a navigation system has become an essential tool for optimizing long road trips by minimizing the number of charging sessions en route to destinations. BEV routing is designed to provide accurate and realistic estimates of the electric driving range, considering the current battery state of charge (SOC) as well as current and future weather and traffic conditions. This is an improvement over the range estimates shown on a vehicle’s dashboard, typically calculated based on the current SOC and historical driving efficiencies stored in an electronic control unit (ECU). BEV routing enhances existing navigation by incorporating estimates of the required battery energy for the shortest or fastest routes to a destination. It uses predefined vehicle energy models that account for traffic, road, and weather attributes, such as speed limits, traffic speed, elevation gain, and ambient temperature, along the selected routes. Additionally, the vehicle energy models can be integrated into route optimizations to provide a comprehensive BEV routing solution, suggesting routes based on the driver’s preferences, such as energy-efficient or time-efficient routes. This paper addresses the shortcomings of conventional BEV routing, where the vehicle energy models do not accurately represent real-world energy consumption due to varying road, weather, and air conditioning (AC) conditions. The authors first utilize a state-of-the-art distributed data processing platform to extract, transform, and load petabytes worth of raw time-series data into position-based data suitable for model development. The authors then propose a novel method for training end-to-end multi-input vehicle energy models directly from this position-based data, along with efficient calculation schemes to enable accurate energy predictions for long-distance trips. The proposed method was validated with real-world data and demonstrated superior performance compared to two benchmark BEV routing solutions currently available.