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 1. 4TU.ResearchData. software. https://doi.org/10.4121/uuid:721b2ea6-2634-4dd5-a0c8-865a0aa41a99
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
Date: 2020-12-22
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
Date: 2020-12-22
history
- 2021-01-14 first online, 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 Engineer
DATA
files (1)
- 1,804,872 bytesMD5:
4d78099d0f1d301b545987db8f8a7ce6
TUe_MECPRO.zip -
download all files (zip)
1,804,872 bytes unzipped