| Summary: | This research combines a set of optimization algorithms with methods of finding and using the expected values of forecasted weather variables to create an optimal speed profile for a solar electric vehicle to minimize energy usage while traveling the furthest distance during the Sasol Solar Challenge 2018 as such work has not been formalized yet. Various Probability Mass Functions describe and apply the probabilistic characteristics of the solar irradiance to the forecasted weather data to increase the probability of better accuracy. These algorithms and Probability Mass Functions are implemented and tested in an international event on South African national roads across a vast distance of 2396 km under varying weather conditions and challenging route topography. The two algorithms under discussion: the first for high-resolution single day optimization of stored energy (by controlling the vehicle speed) and the second for multiple-day lower resolution distance optimization (by controlling the amount of additional road to be traveled each day). The results are meaningful in predicting the energy required by a solar electric vehicle traveling along a route in South Africa, and the importance of the optimal speed requirements to attain the best results is shown to be essential. The authors of this paper believe that implementing this work on the Sun Chaser III solar car, which competed in the Sasol Solar Challenge 2018, owe its position of first place among local teams, and fourth place internationally, to the accuracy and robustness of the work.
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