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Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation
Electric vehicles (EVs) are widely promoted as clean alternatives to conventional vehicles for reducing greenhouse gas (GHG) emissions from ground transportation. However, the battery undergoes a sophisticated degradation process during EV operations and its effects on EV energy consumption and GHG...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013442/ https://www.ncbi.nlm.nih.gov/pubmed/29930259 http://dx.doi.org/10.1038/s41467-018-04826-0 |
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author | Yang, Fan Xie, Yuanyuan Deng, Yelin Yuan, Chris |
author_facet | Yang, Fan Xie, Yuanyuan Deng, Yelin Yuan, Chris |
author_sort | Yang, Fan |
collection | PubMed |
description | Electric vehicles (EVs) are widely promoted as clean alternatives to conventional vehicles for reducing greenhouse gas (GHG) emissions from ground transportation. However, the battery undergoes a sophisticated degradation process during EV operations and its effects on EV energy consumption and GHG emissions are unknown. Here we show on a typical 24 kWh lithium-manganese-oxide–graphite battery pack that the degradation of EV battery can be mathematically modeled to predict battery life and to study its effects on energy consumption and GHG emissions from EV operations. We found that under US state-level average driving conditions, the battery life is ranging between 5.2 years in Florida and 13.3 years in Alaska under 30% battery degradation limit. The battery degradation will cause a 11.5–16.2% increase in energy consumption and GHG emissions per km driven at 30% capacity loss. This study provides a robust analytical approach and results for supporting policy making in prioritizing EV deployment in the U.S. |
format | Online Article Text |
id | pubmed-6013442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-60134422018-06-25 Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation Yang, Fan Xie, Yuanyuan Deng, Yelin Yuan, Chris Nat Commun Article Electric vehicles (EVs) are widely promoted as clean alternatives to conventional vehicles for reducing greenhouse gas (GHG) emissions from ground transportation. However, the battery undergoes a sophisticated degradation process during EV operations and its effects on EV energy consumption and GHG emissions are unknown. Here we show on a typical 24 kWh lithium-manganese-oxide–graphite battery pack that the degradation of EV battery can be mathematically modeled to predict battery life and to study its effects on energy consumption and GHG emissions from EV operations. We found that under US state-level average driving conditions, the battery life is ranging between 5.2 years in Florida and 13.3 years in Alaska under 30% battery degradation limit. The battery degradation will cause a 11.5–16.2% increase in energy consumption and GHG emissions per km driven at 30% capacity loss. This study provides a robust analytical approach and results for supporting policy making in prioritizing EV deployment in the U.S. Nature Publishing Group UK 2018-06-21 /pmc/articles/PMC6013442/ /pubmed/29930259 http://dx.doi.org/10.1038/s41467-018-04826-0 Text en © The Author(s) 2018 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/. |
spellingShingle | Article Yang, Fan Xie, Yuanyuan Deng, Yelin Yuan, Chris Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title | Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title_full | Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title_fullStr | Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title_full_unstemmed | Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title_short | Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation |
title_sort | predictive modeling of battery degradation and greenhouse gas emissions from u.s. state-level electric vehicle operation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013442/ https://www.ncbi.nlm.nih.gov/pubmed/29930259 http://dx.doi.org/10.1038/s41467-018-04826-0 |
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