Planning for charging in transport missions is vital when commercial long-haul vehicles are to be electrified. In this planning, accurate range prediction is essential so the trucks reach their destinations as planned. The rolling resistance significantly influences truck energy consumption, often considered a simple constant or a function of vehicle speed only. This is, however, a gross simplification, especially as the tire temperature has a significant impact. At 80 km/h, a cold tire can have three times higher rolling resistance than a warm tire.A temperature-dependent rolling resistance model is proposed. The model is based on thermal networks for the temperature at four places around the tire. The model is tuned and validated using rolling resistance, tire shoulder, and tire apex temperature measurements with a truck in a climate wind tunnel with ambient temperatures ranging from -30 to 25 °C at an 80 km/h constant speed. Dynamic tire simulations were conducted using a heat transfer model, considering road, ambient, shoulder, and apex temperatures. The simulation results were compared with measured data for ambient, shoulder, and apex temperatures, and the model captures both time constants and stationary levels. The resulting model can predict the dynamics of the rolling resistance and will, therefore, give a more accurate prediction when tires are cold and warming up. Driving range simulations of a long haulage battery-electric truck have also been conducted demonstrating how the range changes with varying ambient temperatures as well as the influence a snapshot consumption has on range estimation.