Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries. Laue, V., Schmidt, O., Dreger, H., Xie, X., Röder, F., Schenkendorf, R., Kwade, A., & Krewer, U. Energy Technology, 1900201:1900201, 4, 2019.
![Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper ![Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 1 download
Paper Website doi abstract bibtex 1 download A model-based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium-ion batteries (LIB). Cell-to-cell deviations and the influence of sub-cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub-cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni 1/3 Co 1/3 Mn 1/3 )O 2 (NMC) and graphite (Li x C 6 ). It is integrated into a sampling-based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C-rate. Furthermore, the sub-cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub-cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.
@article{
title = {Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries},
type = {article},
year = {2019},
keywords = {Li-ion batteries,modeling,production process,uncertainty quantification methods},
pages = {1900201},
volume = {1900201},
websites = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ente.201900201},
month = {4},
day = {29},
id = {539ba7e0-95e2-3255-8dba-b2e0d60f1f78},
created = {2021-10-26T13:36:43.568Z},
file_attached = {true},
profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},
group_id = {cc302413-c146-306b-8008-abbf67f3b420},
last_modified = {2021-10-26T13:36:55.325Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {Laue2019},
private_publication = {false},
abstract = {A model-based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium-ion batteries (LIB). Cell-to-cell deviations and the influence of sub-cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub-cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni 1/3 Co 1/3 Mn 1/3 )O 2 (NMC) and graphite (Li x C 6 ). It is integrated into a sampling-based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C-rate. Furthermore, the sub-cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub-cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.},
bibtype = {article},
author = {Laue, Vincent and Schmidt, Oke and Dreger, Henning and Xie, Xiangzhong and Röder, Fridolin and Schenkendorf, René and Kwade, Arno and Krewer, Ulrike},
doi = {10.1002/ente.201900201},
journal = {Energy Technology}
}Downloads: 1
{"_id":"HCBTCYopNrYBKwenD","bibbaseid":"laue-schmidt-dreger-xie-rder-schenkendorf-kwade-krewer-modelbaseduncertaintyquantificationfortheproductpropertiesoflithiumionbatteries-2019","downloads":1,"creationDate":"2019-04-01T21:47:47.931Z","title":"Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries","author_short":["Laue, V.","Schmidt, O.","Dreger, H.","Xie, X.","Röder, F.","Schenkendorf, R.","Kwade, A.","Krewer, U."],"year":2019,"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/46206c9e-d69a-378c-9478-6dd168f65080","bibdata":{"title":"Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries","type":"article","year":"2019","keywords":"Li-ion batteries,modeling,production process,uncertainty quantification methods","pages":"1900201","volume":"1900201","websites":"https://onlinelibrary.wiley.com/doi/abs/10.1002/ente.201900201","month":"4","day":"29","id":"539ba7e0-95e2-3255-8dba-b2e0d60f1f78","created":"2021-10-26T13:36:43.568Z","file_attached":"true","profile_id":"46206c9e-d69a-378c-9478-6dd168f65080","group_id":"cc302413-c146-306b-8008-abbf67f3b420","last_modified":"2021-10-26T13:36:55.325Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Laue2019","private_publication":false,"abstract":"A model-based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium-ion batteries (LIB). Cell-to-cell deviations and the influence of sub-cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub-cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni 1/3 Co 1/3 Mn 1/3 )O 2 (NMC) and graphite (Li x C 6 ). It is integrated into a sampling-based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C-rate. Furthermore, the sub-cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub-cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.","bibtype":"article","author":"Laue, Vincent and Schmidt, Oke and Dreger, Henning and Xie, Xiangzhong and Röder, Fridolin and Schenkendorf, René and Kwade, Arno and Krewer, Ulrike","doi":"10.1002/ente.201900201","journal":"Energy Technology","bibtex":"@article{\n title = {Model-Based Uncertainty Quantification for the Product Properties of Lithium-Ion Batteries},\n type = {article},\n year = {2019},\n keywords = {Li-ion batteries,modeling,production process,uncertainty quantification methods},\n pages = {1900201},\n volume = {1900201},\n websites = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ente.201900201},\n month = {4},\n day = {29},\n id = {539ba7e0-95e2-3255-8dba-b2e0d60f1f78},\n created = {2021-10-26T13:36:43.568Z},\n file_attached = {true},\n profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},\n group_id = {cc302413-c146-306b-8008-abbf67f3b420},\n last_modified = {2021-10-26T13:36:55.325Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Laue2019},\n private_publication = {false},\n abstract = {A model-based uncertainty quantification (UQ) approach is applied to the manufacturing process of lithium-ion batteries (LIB). Cell-to-cell deviations and the influence of sub-cell level variations in the material and electrode properties of the cell performance are investigated experimentally and via modeling. The electrochemical battery model of the Doyle–Newman type is extended to cover the effect of sub-cell deviation of product properties of the LIB. The applied model is parameterized and validated using a stacked pouch cell containing Li(Ni 1/3 Co 1/3 Mn 1/3 )O 2 (NMC) and graphite (Li x C 6 ). It is integrated into a sampling-based UQ framework. A nested point estimate method (PEM) is applied to a large number of independent normal distributed parameters. The simulations follow two consecutive nonideal manufacturing process steps: coating and calendering. The nested PEM provides a global sensitivity analysis that shows a change in sensitivity of the investigated parameters depending on the applied C-rate. Furthermore, the sub-cell level deviation of parameters in heterogeneous electrodes provokes a nonuniform current distribution in the cell. This alters the variance of the discharge capacity distribution. Therefore, sub-cell deviation has to be considered to quantify process uncertainties. The applied method is feasible and highly efficient for this purpose.},\n bibtype = {article},\n author = {Laue, Vincent and Schmidt, Oke and Dreger, Henning and Xie, Xiangzhong and Röder, Fridolin and Schenkendorf, René and Kwade, Arno and Krewer, Ulrike},\n doi = {10.1002/ente.201900201},\n journal = {Energy Technology}\n}","author_short":["Laue, V.","Schmidt, O.","Dreger, H.","Xie, X.","Röder, F.","Schenkendorf, R.","Kwade, A.","Krewer, U."],"urls":{"Paper":"https://bibbase.org/service/mendeley/46206c9e-d69a-378c-9478-6dd168f65080/file/1f6ae1cb-09e2-5105-26f6-d4722e9ccef0/Laue_et_al_2019_Energy_Technology_2.pdf.pdf","Website":"https://onlinelibrary.wiley.com/doi/abs/10.1002/ente.201900201"},"biburl":"https://bibbase.org/service/mendeley/46206c9e-d69a-378c-9478-6dd168f65080","bibbaseid":"laue-schmidt-dreger-xie-rder-schenkendorf-kwade-krewer-modelbaseduncertaintyquantificationfortheproductpropertiesoflithiumionbatteries-2019","role":"author","keyword":["Li-ion batteries","modeling","production process","uncertainty quantification methods"],"metadata":{"authorlinks":{"krewer, u":"https://bibbase.org/show?bib=https%3A%2F%2Fbibbase.org%2Fzotero%2FInES_TUBS&msg=embed"}},"downloads":1},"search_terms":["model","based","uncertainty","quantification","product","properties","lithium","ion","batteries","laue","schmidt","dreger","xie","röder","schenkendorf","kwade","krewer"],"keywords":["li-ion batteries","modeling","production process","uncertainty quantification methods"],"authorIDs":["M5XZkuDC9FL7QRGmS"],"dataSources":["67CvPrRggJ3tpQYM9","ya2CyA73rpZseyrZ8"]}