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
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choose version: version 3 - 2021-08-24 (latest) version 2 - 2021-06-23
version 1 - 2021-01-14
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
  • 2021-01-14 first online, published, posted
media types: text/plain
  • Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management (grant code 713771) [more info...] European Commission
Eindhoven University of Technology, Department of Mechanical Engineer


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