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An open tool for creating battery-electric vehicle time series from empirical data, emobpy

Engineering and Technology

An open tool for creating battery-electric vehicle time series from empirical data, emobpy

C. Gaete-morales, H. Kramer, et al.

Discover emobpy, an innovative open-source tool transforming battery-electric vehicle profiles using empirical mobility data. Developed by Carlos Gaete-Morales, Hendrik Kramer, Wolf-Peter Schill, and Alexander Zerrahn, this tool generates essential time series data for BEV mobility and grid demand, revealing critical insights into grid availability and charging strategies in Germany.

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~3 min • Beginner • English
Abstract
There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.
Publisher
Scientific Data
Published On
Jun 11, 2021
Authors
Carlos Gaete-Morales, Hendrik Kramer, Wolf-Peter Schill, Alexander Zerrahn
Tags
battery-electric vehicles
empirical mobility statistics
grid electricity demand
time series data
charging strategies
grid availability
energy models
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