Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprisin...

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Published inNature communications Vol. 13; no. 1; pp. 2261 - 10
Main Authors Zhu, Jiangong, Wang, Yixiu, Huang, Yuan, Bhushan Gopaluni, R., Cao, Yankai, Heere, Michael, Mühlbauer, Martin J., Mereacre, Liuda, Dai, Haifeng, Liu, Xinhua, Senyshyn, Anatoliy, Wei, Xuezhe, Knapp, Michael, Ehrenberg, Helmut
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 27.04.2022
Nature Publishing Group
Nature Portfolio
Subjects
639/166/987
639/301/299
639/4077/4079/891
639/705/1046
Cycles
Datasets
Electric cells
Electric potential
Electrodes
Humanities and Social Sciences
Linear transformations
Lithium
Lithium-ion batteries
Machine learning
multidisciplinary
Rechargeable batteries
Root-mean-square errors
Science
Science (multidisciplinary)
Transfer learning
Voltage
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Abstract Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi 0.86 Co 0.11 Al 0.03 O 2 -based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi 0.83 Co 0.11 Mn 0.07 O 2 -based positive electrodes and batteries with the blend of Li(NiCoMn)O 2 - Li(NiCoAl)O 2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation. Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.
AbstractList Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.
Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.
Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.
Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi 0.86 Co 0.11 Al 0.03 O 2 -based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi 0.83 Co 0.11 Mn 0.07 O 2 -based positive electrodes and batteries with the blend of Li(NiCoMn)O 2 - Li(NiCoAl)O 2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation. Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from the relaxation voltage curve for battery capacity estimation without requiring other previous cycling information.
Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi 0.86 Co 0.11 Al 0.03 O 2 -based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi 0.83 Co 0.11 Mn 0.07 O 2 -based positive electrodes and batteries with the blend of Li(NiCoMn)O 2 - Li(NiCoAl)O 2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.
Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi Co Al O -based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi Co Mn O -based positive electrodes and batteries with the blend of Li(NiCoMn)O - Li(NiCoAl)O positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.
ArticleNumber 2261
Author Ehrenberg, Helmut
Senyshyn, Anatoliy
Heere, Michael
Knapp, Michael
Mühlbauer, Martin J.
Cao, Yankai
Wang, Yixiu
Liu, Xinhua
Zhu, Jiangong
Dai, Haifeng
Wei, Xuezhe
Bhushan Gopaluni, R.
Mereacre, Liuda
Huang, Yuan
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  givenname: Jiangong
  orcidid: 0000-0002-3780-4286
  surname: Zhu
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  organization: Clean Energy Automotive Engineering Center, School of Automotive Engineering, Tongji University, Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT)
– sequence: 2
  givenname: Yixiu
  surname: Wang
  fullname: Wang, Yixiu
  organization: Department of Chemical and Biological Engineering, University of British Columbia
– sequence: 3
  givenname: Yuan
  surname: Huang
  fullname: Huang, Yuan
  organization: Clean Energy Automotive Engineering Center, School of Automotive Engineering, Tongji University, Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT)
– sequence: 4
  givenname: R.
  surname: Bhushan Gopaluni
  fullname: Bhushan Gopaluni, R.
  organization: Department of Chemical and Biological Engineering, University of British Columbia
– sequence: 5
  givenname: Yankai
  surname: Cao
  fullname: Cao, Yankai
  organization: Department of Chemical and Biological Engineering, University of British Columbia
– sequence: 6
  givenname: Michael
  surname: Heere
  fullname: Heere, Michael
  organization: Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT), Technische Universität Braunschweig, Institute of Internal Combustion Engines
– sequence: 7
  givenname: Martin J.
  surname: Mühlbauer
  fullname: Mühlbauer, Martin J.
  organization: Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT)
– sequence: 8
  givenname: Liuda
  surname: Mereacre
  fullname: Mereacre, Liuda
  organization: Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT)
– sequence: 9
  givenname: Haifeng
  orcidid: 0000-0001-5322-2019
  surname: Dai
  fullname: Dai, Haifeng
  email: tongjidai@tongji.edu.cn
  organization: Clean Energy Automotive Engineering Center, School of Automotive Engineering, Tongji University
– sequence: 10
  givenname: Xinhua
  orcidid: 0000-0002-4111-7235
  surname: Liu
  fullname: Liu, Xinhua
  organization: School of Transportation Science and Engineering, Beihang University
– sequence: 11
  givenname: Anatoliy
  orcidid: 0000-0002-1473-8992
  surname: Senyshyn
  fullname: Senyshyn, Anatoliy
  organization: Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, Lichtenbergstr. 1, 85748 Garching b
– sequence: 12
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  fullname: Wei, Xuezhe
  organization: Clean Energy Automotive Engineering Center, School of Automotive Engineering, Tongji University
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  givenname: Michael
  orcidid: 0000-0003-0091-8463
  surname: Knapp
  fullname: Knapp, Michael
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  surname: Ehrenberg
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  organization: Institute for Applied Materials (IAM), Karlsruhe Institute of Technology (KIT)
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35477711$$D View this record in MEDLINE/PubMed
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Snippet Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve...
Accurate capacity estimation is crucial for lithium-ion batteries' reliable and safe operation. Here, the authors propose an approach exploiting features from...
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SubjectTerms 639/166/987
639/301/299
639/4077/4079/891
639/705/1046
Cycles
Datasets
Electric cells
Electric potential
Electrodes
Humanities and Social Sciences
Linear transformations
Lithium
Lithium-ion batteries
Machine learning
multidisciplinary
Rechargeable batteries
Root-mean-square errors
Science
Science (multidisciplinary)
Transfer learning
Voltage
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Title Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
URI https://link.springer.com/article/10.1038/s41467-022-29837-w
https://www.ncbi.nlm.nih.gov/pubmed/35477711
https://www.proquest.com/docview/2655924788
https://www.proquest.com/docview/2656745570
https://pubmed.ncbi.nlm.nih.gov/PMC9046220
https://doaj.org/article/0d2de2d9a30c498789b5e17adb2f089b
Volume 13
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