TY - DATA
T1 - TU/e Microscopic Energy Consumption PRediction tOol 0.1   (TU/e MECPRO 0.1)
PY - 2021/01/14
AU - Camiel Beckers
AU - Tim A.G.H. Geraedts
AU - I.J.M. (Igo) Besselink
AU - H. (Henk) Nijmeijer
UR - https://data.4tu.nl/articles/software/TU_e_Microscopic_Energy_Consumption_PRediction_tOol_0_1_TU_e_MECPRO_0_1_/12764732/1
DO - 10.4121/uuid:721b2ea6-2634-4dd5-a0c8-865a0aa41a99
KW - Automotive Engineering
KW - Environmentally Sustainable Transport
KW - Ground Transport
KW - Electric Vehicle (EV)
KW - Energy consumption
KW - Battery Electric Vehicle
N2 - 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).<br> <br>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. <br><br>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).<br>Version: 0.1 <br>Date:    2020-12-22<br>
ER -