Model Factsheet

General Information

Name	An open tool for creating battery-electric vehicle time series from empirical data
Acronym	emobpy
Methodical Focus	Simulation
Institution(s)	DIW Berlin (German Institute of Economic Research)
Author(s) (institution, working field, active time period)
Current contact person	Carlos Gaete-Morales
Contact (e-mail)	cdgaete@gmail.com
Website	https://pypi.org/project/emobpy/
Logo
Primary Purpose	emobpy is a Python tool that can create battery electric vehicle time series. Four different time series can be created: vehicle mobility time series, driving electricity consumption time series, grid availability time series and grid electricity demand time series. The vehicles mobility time series are created based on mobility statistics. For driving electricity consumption time series, the properties of vehicles can be selected from a database with several actual battery electric vehicles models. emobpy is developed by the research group Transformation of the Energy Economy at DIW Berlin (German Institute of Economic Research).
Primary Outputs
Support / Community / Forum
Framework
Link to User Documentation	-
Link to Developer/Code Documentation	-
Documentation quality	expandable
Source of funding	-
Number of developers	-
Number of users	-

Openness

Open Source
License	MIT
Source code available
GitHub
Access to source code	https://gitlab.com/diw-evu/emobpy/emobpy/
Data provided	example data
Collaborative programming

Software

Modelling software	Python
Internal data processing software
External optimizer
Additional software
GUI

Coverage

Modeled energy sectors (final energy)	-
Modeled demand sectors	-
Modeled technologies: components for power generation or conversion	Renewables - Conventional -
Modeled technologies: components for transfer, infrastructure or grid	Electricity - Gas - Heat -
Properties electrical grid	-
Modeled technologies: components for storage	-
User behaviour and demand side management
Changes in efficiency
Market models	-
Geographical coverage
Geographic (spatial) resolution	-
Time resolution	-
Comment on geographic (spatial) resolution
Observation period	-
Additional dimensions (sector)	-

Mathematical Properties

Model class (optimisation)	-
Model class (simulation)	-
Other
Short description of mathematical model class
Mathematical objective	-
Approach to uncertainty	-
Suited for many scenarios / monte-carlo
typical computation time	-
Typical computation hardware	-
Technical data anchored in the model	-

Model Integration

Interfaces
Model file format	.exe
Input data file format	.csv
Output data file format	.csv
Integration with other models
Integration of other models

References

Citation reference	-
Citation DOI	-
Reference Studies/Models	-
Example research questions	-
Model usage	-
Model validation	-
Example research questions	-
further properties
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Tags

grid time series MIT