TU/e Microscopic Energy Consumption PRediction tOol 0.1 (TU/e MECPRO 0.1)
Datacite citation style:
Beckers, Camiel; Tim A.G.H. Geraedts; Besselink, I.J.M. (Igo); Nijmeijer, H. (Henk) (2021): TU/e Microscopic Energy Consumption PRediction tOol 0.1 (TU/e MECPRO 0.1). Version 2. 4TU.ResearchData. software. https://doi.org/10.4121/12764732.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
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: 0.1.2
Date: 2021-06-23
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'.
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: 0.1.2
Date: 2021-06-23
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'.
history
- 2021-01-14 first online
- 2021-06-23 published, posted
publisher
4TU.ResearchData
format
media types: text/plain
associated peer-reviewed publication
A Microscopic Energy Consumption Prediction Tool for Fully Electric Delivery Vans
funding
- Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management (grant code 713771) [more info...] European Commission
organizations
Eindhoven University of Technology, Department of Mechanical Engineering
DATA
files (1)
- 4,710,673 bytesMD5:
ee397011309c641257a03f54220c1e43
TUe_MECPRO_0.1.2.zip -
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