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Impedance-based forecasting of lithium-ion battery performance amid uneven usage

Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. H...

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Autores principales: Jones, Penelope K., Stimming, Ulrich, Lee, Alpha A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381522/
https://www.ncbi.nlm.nih.gov/pubmed/35974010
http://dx.doi.org/10.1038/s41467-022-32422-w
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author Jones, Penelope K.
Stimming, Ulrich
Lee, Alpha A.
author_facet Jones, Penelope K.
Stimming, Ulrich
Lee, Alpha A.
author_sort Jones, Penelope K.
collection PubMed
description Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health.
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spelling pubmed-93815222022-08-18 Impedance-based forecasting of lithium-ion battery performance amid uneven usage Jones, Penelope K. Stimming, Ulrich Lee, Alpha A. Nat Commun Article Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health. Nature Publishing Group UK 2022-08-16 /pmc/articles/PMC9381522/ /pubmed/35974010 http://dx.doi.org/10.1038/s41467-022-32422-w Text en © The Author(s) 2022 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
Jones, Penelope K.
Stimming, Ulrich
Lee, Alpha A.
Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title_full Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title_fullStr Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title_full_unstemmed Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title_short Impedance-based forecasting of lithium-ion battery performance amid uneven usage
title_sort impedance-based forecasting of lithium-ion battery performance amid uneven usage
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381522/
https://www.ncbi.nlm.nih.gov/pubmed/35974010
http://dx.doi.org/10.1038/s41467-022-32422-w
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