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An open tool for creating battery-electric vehicle time series from empirical data, emobpy
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 electricit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196066/ https://www.ncbi.nlm.nih.gov/pubmed/34117257 http://dx.doi.org/10.1038/s41597-021-00932-9 |
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author | Gaete-Morales, Carlos Kramer, Hendrik Schill, Wolf-Peter Zerrahn, Alexander |
author_facet | Gaete-Morales, Carlos Kramer, Hendrik Schill, Wolf-Peter Zerrahn, Alexander |
author_sort | Gaete-Morales, Carlos |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8196066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81960662021-06-17 An open tool for creating battery-electric vehicle time series from empirical data, emobpy Gaete-Morales, Carlos Kramer, Hendrik Schill, Wolf-Peter Zerrahn, Alexander Sci Data Article 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. Nature Publishing Group UK 2021-06-11 /pmc/articles/PMC8196066/ /pubmed/34117257 http://dx.doi.org/10.1038/s41597-021-00932-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gaete-Morales, Carlos Kramer, Hendrik Schill, Wolf-Peter Zerrahn, Alexander An open tool for creating battery-electric vehicle time series from empirical data, emobpy |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196066/ https://www.ncbi.nlm.nih.gov/pubmed/34117257 http://dx.doi.org/10.1038/s41597-021-00932-9 |
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