Energy efficient route prediction for solar powered vehicles

Abstract

Solar powered vehicles are currently being developed towards entirely self-sustaining vehicles that harness their energy directly from the sun. For such vehicles, it is important to optimise their solar exposure while driving, thereby reducing their energy consumption through fossil fuels. Research has emerged to estimate optimised routes for solar vehicles, and this paper builds on this work to expand on the parameters used to calculate the route, thereby improving the energy-harnessing quality of the route together with its overall utility for the driver. The ArcGIS tool and the open weather API are used to predict the solar potential of a vehicle by taking into account shade based on surrounding topography, vehicle type, weather, distance and time of day. The model was implemented as a user mobile application ‘Drive Solar’ that calculates the optimal route for the user based on their preferences for time and energy efficiency. The effectiveness of the prediction model was tested using a solar irradiance sensor in Dublin city. The results show that the model predicts the route with the most energy absorbed with a 51.65% accuracy and chooses the route with the most energy consumed with a 86.65% accuracy. We conclude that Drive Solar can aid in the transition to widespread use of self-sustaining solar vehicles.