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

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 e...

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Autores principales:	Carlos Gaete-Morales, Hendrik Kramer, Wolf-Peter Schill, Alexander Zerrahn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/eaf57fb1ac824baa889cf10a01c1c0ae
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id	oai:doaj.org-article:eaf57fb1ac824baa889cf10a01c1c0ae
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spelling	oai:doaj.org-article:eaf57fb1ac824baa889cf10a01c1c0ae2021-12-02T17:47:16ZAn open tool for creating battery-electric vehicle time series from empirical data, emobpy10.1038/s41597-021-00932-92052-4463https://doaj.org/article/eaf57fb1ac824baa889cf10a01c1c0ae2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41597-021-00932-9https://doaj.org/toc/2052-4463Abstract 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.Carlos Gaete-MoralesHendrik KramerWolf-Peter SchillAlexander ZerrahnNature PortfolioarticleScienceQENScientific Data, Vol 8, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Carlos Gaete-Morales Hendrik Kramer Wolf-Peter Schill Alexander Zerrahn An open tool for creating battery-electric vehicle time series from empirical data, emobpy
description	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.
format	article
author	Carlos Gaete-Morales Hendrik Kramer Wolf-Peter Schill Alexander Zerrahn
author_facet	Carlos Gaete-Morales Hendrik Kramer Wolf-Peter Schill Alexander Zerrahn
author_sort	Carlos Gaete-Morales
title	An open tool for creating battery-electric vehicle time series from empirical data, emobpy
title_short	An open tool for creating battery-electric vehicle time series from empirical data, emobpy
title_full	An open tool for creating battery-electric vehicle time series from empirical data, emobpy
title_fullStr	An open tool for creating battery-electric vehicle time series from empirical data, emobpy
title_full_unstemmed	An open tool for creating battery-electric vehicle time series from empirical data, emobpy
title_sort	open tool for creating battery-electric vehicle time series from empirical data, emobpy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eaf57fb1ac824baa889cf10a01c1c0ae
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An open tool for creating battery-electric vehicle time series from empirical data, emobpy

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