• Research softwareclose
• 7. Clean energy close

The MATLAB-files contained within this dataset describe a nonlinear steady-state cornering model for a six-wheel city bus. Due to the employed multi-body approach, the wheel-configuration of the model can be changed easily. The wheel location and orientation is completely parameterized, allowing for the analysis of different types of vehicles. Emphasis is placed on the calculation of the cornering resistance power and power lost due to scrub losses to research the effect of these tire-effect on the vehicle energy consumption. The current model includes six wheels, where the two sets of double rear wheels have individual rotational velocities. In the model derivation, linearizations are avoided: large angles are allowed and the non-linear Magic Formula is employed to calculate the tire forces. Additionally, lateral load transfer effects, due to the elevated center of gravity (CoG), are included. The developed non-linear model has four degrees of freedom. Steady-state solutions of the model are determined iteratively using an adapted Newton scheme. The model enables the calculation of all tire velocities and tire forces for a given cornering situation characterized by the cornering radius rho and the vehicle velocity v.This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 713771 (EVERLASTING).

The files contained within this dataset describe a simulation tool that predicts the energy consumption of a battery electric vehicle. The tool is written in MATLAB-code and is connected to various API's to make use of up-to-date route information (Overpass OpenStreetMap API), height information (SRTM elevation map), and weather information (OpenWeatherMap API). The prediction method relies on a physics-based interpretation of the energy consumption of the vehicle. Both the velocity profile prediction algorithm and the subsequent energy consumption model are based on data obtained from dedicated vehicle tests. In the supplied version of this tool, the parameters represent the Voltia eVan, which is a fully electric delivery van with a swappable traction battery. The tool was developed within the Dynamics & Control research-group at Eindhoven University of Technology. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 713771 (EVERLASTING).Version: 0.1.3 Date: 2021-08-23Change Log Version 0.1.3------------------------- Updated 'readhght' from François Beauducel, which again omits the use of the NASA/EarthDATA account that was introduced in the previous version. SRTM elevation data can now again be obtained without account.Change Log Version 0.1.2------------------------- Changed the inputs to 'readhght' to use the NASA/EarthDATA server, because the original server hosting the SRTM DEM appears to be off-line. This new server requires a NASA/EarthDATA account. If road slope is to be included, 'settings.includeSlope' (l. 76, TUe_MECPRO.m) should be set to 'true' and NASA/EarthDATA account credentials should be entered in 'NASA_Earthdata_Login.txt'.- Corrected an error in velocityProfilePredicion.m where the prescribed maximum vehicle velocity was wrongly considered to be [m/s] instead of [km/h].- Corrected an error in processOSMmap.m that resulted in the incorrect registration of the maximum legislated velocity, in case it is prescribed as 'none' (German Highways). Now, 200km/h is assumed in case the maximum legislated velocity is 'none'.