PGD based meta modelling of a lithium-ion battery for real time prediction
Despite the existence of computationally efficient tools, the effort for parametric investigations is currently high in industry. In this paper, within the context of Li-Ion batteries, an efficient meta-modelling approach based on the Proper Generalized Decomposition (PGD) is considered. From a suit...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Materials |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmats.2023.1245347/full |
| _version_ | 1827866055988477952 |
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| author | Alexander Schmid Angelo Pasquale Angelo Pasquale Christian Ellersdorfer Victor Champaney Victor Champaney Marco Raffler Simon Guévelou Stephan Kizio Mustapha Ziane Florian Feist Francisco Chinesta Francisco Chinesta Francisco Chinesta |
| author_facet | Alexander Schmid Angelo Pasquale Angelo Pasquale Christian Ellersdorfer Victor Champaney Victor Champaney Marco Raffler Simon Guévelou Stephan Kizio Mustapha Ziane Florian Feist Francisco Chinesta Francisco Chinesta Francisco Chinesta |
| author_sort | Alexander Schmid |
| collection | DOAJ |
| description | Despite the existence of computationally efficient tools, the effort for parametric investigations is currently high in industry. In this paper, within the context of Li-Ion batteries, an efficient meta-modelling approach based on the Proper Generalized Decomposition (PGD) is considered. From a suitable design of experiments, a parametric model is trained and then exploited to predict, in real time, the system response to a specific parameter combination. In particular, two different methods are considered, the sparse PGD (sPGD) and the anchored-ANOVA based one (ANOVA-PGD). As a use case for the method the dynamic indentation test of a commercial lithium-ion pouch cell with a cylindrical impactor is selected. The cell model considers a homogenised macroscopic structure suitably calibrated for explicit finite element simulations. Four parameters concerning the impactor are varied, both non-geometric (mass and initial velocity) and geometric (diameter and orientation). The study focuses on multi-dimensional outputs, such as curves and contour plots. Inspired by earlier studies, the sPGD is used to predict the force-displacement curves. As a further development, the impactor kinetic energy curve and the displacement contours are both predicted using its recently developed variant ANOVA-PGD. Moreover, a novel curve alignment technique based on the Gappy Proper Orthogonal Decomposition (Gappy-POD) is suggested here. The meta-model is compared to the results of an FE simulation and the resulting deviations are then discussed. |
| first_indexed | 2024-03-12T14:57:27Z |
| format | Article |
| id | doaj.art-50bf9f3f5dda4f29b156a697101fc544 |
| institution | Directory Open Access Journal |
| issn | 2296-8016 |
| language | English |
| last_indexed | 2024-03-12T14:57:27Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Materials |
| spelling | doaj.art-50bf9f3f5dda4f29b156a697101fc5442023-08-14T12:44:56ZengFrontiers Media S.A.Frontiers in Materials2296-80162023-08-011010.3389/fmats.2023.12453471245347PGD based meta modelling of a lithium-ion battery for real time predictionAlexander Schmid0Angelo Pasquale1Angelo Pasquale2Christian Ellersdorfer3Victor Champaney4Victor Champaney5Marco Raffler6Simon Guévelou7Stephan Kizio8Mustapha Ziane9Florian Feist10Francisco Chinesta11Francisco Chinesta12Francisco Chinesta13Vehicle Safety Institute, Graz University of Technology, Graz, AustriaPIMM Lab, ENSAM Institute of Technology, Paris, FranceESI Group Chair, ENSAM Institute of Technology, Paris, FranceVehicle Safety Institute, Graz University of Technology, Graz, AustriaPIMM Lab, ENSAM Institute of Technology, Paris, FranceESI Group Chair, ENSAM Institute of Technology, Paris, FranceVehicle Safety Institute, Graz University of Technology, Graz, AustriaESI Group, Paris, FranceAudi, Neckarsulm, GermanyESI Group, Paris, FranceVehicle Safety Institute, Graz University of Technology, Graz, AustriaPIMM Lab, ENSAM Institute of Technology, Paris, FranceESI Group Chair, ENSAM Institute of Technology, Paris, FranceESI Group, Paris, FranceDespite the existence of computationally efficient tools, the effort for parametric investigations is currently high in industry. In this paper, within the context of Li-Ion batteries, an efficient meta-modelling approach based on the Proper Generalized Decomposition (PGD) is considered. From a suitable design of experiments, a parametric model is trained and then exploited to predict, in real time, the system response to a specific parameter combination. In particular, two different methods are considered, the sparse PGD (sPGD) and the anchored-ANOVA based one (ANOVA-PGD). As a use case for the method the dynamic indentation test of a commercial lithium-ion pouch cell with a cylindrical impactor is selected. The cell model considers a homogenised macroscopic structure suitably calibrated for explicit finite element simulations. Four parameters concerning the impactor are varied, both non-geometric (mass and initial velocity) and geometric (diameter and orientation). The study focuses on multi-dimensional outputs, such as curves and contour plots. Inspired by earlier studies, the sPGD is used to predict the force-displacement curves. As a further development, the impactor kinetic energy curve and the displacement contours are both predicted using its recently developed variant ANOVA-PGD. Moreover, a novel curve alignment technique based on the Gappy Proper Orthogonal Decomposition (Gappy-POD) is suggested here. The meta-model is compared to the results of an FE simulation and the resulting deviations are then discussed.https://www.frontiersin.org/articles/10.3389/fmats.2023.1245347/fullproper generalized decompositionparametric modelsnonlinear regressionlithium-ion batteryreal time simulation |
| spellingShingle | Alexander Schmid Angelo Pasquale Angelo Pasquale Christian Ellersdorfer Victor Champaney Victor Champaney Marco Raffler Simon Guévelou Stephan Kizio Mustapha Ziane Florian Feist Francisco Chinesta Francisco Chinesta Francisco Chinesta PGD based meta modelling of a lithium-ion battery for real time prediction Frontiers in Materials proper generalized decomposition parametric models nonlinear regression lithium-ion battery real time simulation |
| title | PGD based meta modelling of a lithium-ion battery for real time prediction |
| title_full | PGD based meta modelling of a lithium-ion battery for real time prediction |
| title_fullStr | PGD based meta modelling of a lithium-ion battery for real time prediction |
| title_full_unstemmed | PGD based meta modelling of a lithium-ion battery for real time prediction |
| title_short | PGD based meta modelling of a lithium-ion battery for real time prediction |
| title_sort | pgd based meta modelling of a lithium ion battery for real time prediction |
| topic | proper generalized decomposition parametric models nonlinear regression lithium-ion battery real time simulation |
| url | https://www.frontiersin.org/articles/10.3389/fmats.2023.1245347/full |
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