The commercialization of solar-powered cars has been a long time coming, but the development of entirely self-sustaining vehicles that harness their energy directly from the sun is still ongoing. For such vehicles, it is important to maximize their solar exposure while driving to optimize their energy usage.
Building on the research work on estimating optimized routes for solar vehicles, scientists at the Trinity College in Dublin, Ireland, have expanded 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 researchers used the online geographic information system software ArcGIS and the open-source application API 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. They implemented the model 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. The results showed 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 an 86.65% accuracy.
While these illustrate that the model can effectively predict the optimal energy-efficient route for a solar vehicle, the researchers have also highlighted some of its limitations. Namely, the model varies in accuracy depending on weather conditions with the most accuracy in clear sky conditions and the least accuracy in medium cloud conditions.
Noting that further research could focus on improving the accuracy of this model, the researchers conclude that “the Drive Solar application effectively implements the prediction model into a user-friendly application so that choosing the optimal route for a solar vehicle becomes a widespread practice as solar vehicles are released.”
Their findings were discussed in “Energy efficient route prediction for solar powered vehicles ,” which was recently published in Green Energy and Intelligent Transport.
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