Meteomatics AG, the Swiss-based global weather company and provider of industry-leading global solar forecasts, is delighted to announce its continued sponsorship of the Tshwane University’s solar-powered car race team: Sunchaser 4.

Meteomatics weather API to be used by Sunchaser 4 solar car

Tshwane University's solar car team competes in the Sasal Solar Challenge, in South Africa, which is one of the most challenging solar car races in the world, with the route bringing challenging weather conditions and terrain.

“We’re proud to be continuing our support over the coming season, by providing access to Meteomatics' Weather API as the Sunchaser Team competes in the Ilanga Cup and next year's SASOL Challenge 2022”, said Alex Longden: Head of Marketing at Meteomatics.

The Sunchaser achieved 1st place amongst South African teams and fourth place internationally, the last time the bi-annual race took place was in 2018. “The key to the success of the car is planning and managing the energy consumption of the vehicle. The Sunchaser Team has created an energy optimization algorithm that combines environmental information with Meteomatics Weather forecasts, which has a phenomenal impact on performance" said Dr Christiaan Oosthuizen, Sunchaser Team Project Manager.

Tshwane University has published two papers that review the impact of Meteomatics' Weather API on their solar cars' race performance.

Paper 1:Solar Electric Vehicle Energy Optimization for the Sasol Solar Challenge 2018: https://ieeexplore.ieee.org/document/8918287

Abstract: the international solar electric vehicle competition space is challenged with energy optimization algorithms in combination with expert forecast data from Meteomatics AG. The paper explains how the Meteomatics AG API is used in a mobile application throughout South Africa to predict potential energy harvesting for an eight-day forecast. Several variables are retrieved from the Meteomatics AG API including the GHI and Total Cloud Cover. The performance results of the solar car are phenomenal and the robust weather predictions were an integral part of the energy system.

Paper 2: The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car (2020): https://www.mdpi.com/1996-1073/13/8/1984

Abstract: Meteomatics AG tailor their weather forecasts to meet the requirements of customers in wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

Interested in hearing more?

Watch Dr Christiaan Oosthuizen's (Project Manager of Tshwane University's Solar Race Car Team) explain how Meteomatics accurate & hyper-localized weather forecasts are critical to his team's race performance