Solar Electric Vehicle Energy Optimization for the Sasol Solar Challenge 2018

Abstract

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.INDEX TERMS Optimization, probability distribution, mathematical model, electric vehicles, sun chaser III, sasol solar challenge 2018.

The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

“…Figure 4 shows the SCIII solar car followed by its telemetry support vehicle responsible for recording and analyzing data during the SSC 2018 [30]. These mean forecast performance std values subsequently confirm the expected increasing error trend with the increase in cloud cover [21].…”

“…The team had developed an energy simulation platform to assist the energy manager in making informed decisions in regards to the speed and distance they should travel each day based on the dynamics of the car, the route to be driven, the weather conditions, and other characteristics such as remaining stored battery energy. The authors [30] showed simulated SoC results compared to real-life recorded data during the SSC 2018 for SCIII for five of the eight days. The remaining three days' data was not recorded due to technical difficulties.…”

“…Figure 4 shows the SCIII solar car followed by its telemetry support vehicle responsible for recording and analyzing data during the SSC 2018 [30]. In 2018, these SoC simulations did not make use of the GMOS model described in this work.…”

“…In 2018, these SoC simulations did not make use of the GMOS model described in this work. Figures 5-7 show how the GMOS model could have improved the GHI forecast accuracy for the same data as the authors used in [30] and how this relates to an improvement in the accuracy of the predicted SoC for SCIII during the SSC 2018. The authors referred to three specific days during the SSC 2018: day one, six, and eight.…”

“…This resulted in the GHI forecast accuracy to be one of the most significant contributors to the remaining SoC errors observed on these days. Table 7 provides a summary of all five days during the SSC 2018 recorded by the TUT team and SCIII [30].…”