Electrically assisted bicycles are emerging as a sustainable and eco-friendly solution in transportation. Range anxiety and poor understanding of the energy expenditure required for a specific route are some of the barriers limiting wider adoption. The current work serves as a proof of concept for an e-bike digital twin designed to address these issues. The digital twin’s input signals are GPS coordinates and altitude recorded with a smartphone and charging power recorded with a smartplug, as well as weather conditions typically available online. First, a theoretical model for the dynamics and the energy exchange of the bicycle is formulated based on literature. Then a methodology for the fit of unknown parameters with dedicated tests is applied. Finally the digital twin is validated in the field and its range estimate is compared to the actual distance traveled during a full discharge consisting of several trips. Possible improvements are discussed, as well as the potential of the digital twin to predict range under transient road and weather conditions, achieving better accuracy than the last leg projections currently used in e-bike displays.