Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity
Electrode manufacturing process strongly impacts lithium-ion battery characteristics. The electrode slurry properties and the coating parameters are among the main factors influencing the electrode heterogeneity which impacts the battery cell performance and lifetime. However, the analysis of the im...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-09-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546821000446 |
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| author | Marc Duquesnoy Iker Boyano Larraitz Ganborena Pablo Cereijo Elixabete Ayerbe Alejandro A. Franco |
| author_facet | Marc Duquesnoy Iker Boyano Larraitz Ganborena Pablo Cereijo Elixabete Ayerbe Alejandro A. Franco |
| author_sort | Marc Duquesnoy |
| collection | DOAJ |
| description | Electrode manufacturing process strongly impacts lithium-ion battery characteristics. The electrode slurry properties and the coating parameters are among the main factors influencing the electrode heterogeneity which impacts the battery cell performance and lifetime. However, the analysis of the impact of electrode manufacturing parameters on the electrode heterogeneity is difficult to be quantified and automatized due to the large number of parameters that can be adjusted in the process. In this work, a data-driven methodology was developed for automatic assessment of the impact of parameters such as the formulation and liquid-to-solid ratio in the slurry, and the gap used for its coating on the current collector, on the electrodes heterogeneity. A dataset generated by experimental measurements was used for training and testing a Machine Learning (ML) classifier namely Gaussian Naives Bayes algorithm, for predicting if an electrode is homogeneous or heterogeneous depending on the manufacturing parameters. Lastly, through a 2D representation, the impact of the manufacturing parameters on the electrode heterogeneity was assessed in detail, paving the way towards a powerful tool for the optimization of next generation of battery electrodes. |
| first_indexed | 2024-12-22T03:57:31Z |
| format | Article |
| id | doaj.art-59aea2437ec140cb99ff1c96869177cf |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-12-22T03:57:31Z |
| publishDate | 2021-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-59aea2437ec140cb99ff1c96869177cf2022-12-21T18:39:50ZengElsevierEnergy and AI2666-54682021-09-015100090Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneityMarc Duquesnoy0Iker Boyano1Larraitz Ganborena2Pablo Cereijo3Elixabete Ayerbe4Alejandro A. Franco5Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France; Reseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France; ALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, 15, rue Baudelocque, Amiens Cedex, 80039, FranceCIDETEC, Basque Research and Technology Alliance (BRTA), P°Miramón 196, 20014 Donostia-San Sebastian, SpainCIDETEC, Basque Research and Technology Alliance (BRTA), P°Miramón 196, 20014 Donostia-San Sebastian, SpainCIDETEC, Basque Research and Technology Alliance (BRTA), P°Miramón 196, 20014 Donostia-San Sebastian, SpainALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, 15, rue Baudelocque, Amiens Cedex, 80039, France; CIDETEC, Basque Research and Technology Alliance (BRTA), P°Miramón 196, 20014 Donostia-San Sebastian, SpainCorresponding author at: Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.; Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France; Reseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France; ALISTORE-European Research Institute, FR CNRS 3104, Hub de l’Energie, 15, rue Baudelocque, Amiens Cedex, 80039, France; Institut Universitaire de France, 103 Boulevard Saint Michel, 75005 Paris, FranceElectrode manufacturing process strongly impacts lithium-ion battery characteristics. The electrode slurry properties and the coating parameters are among the main factors influencing the electrode heterogeneity which impacts the battery cell performance and lifetime. However, the analysis of the impact of electrode manufacturing parameters on the electrode heterogeneity is difficult to be quantified and automatized due to the large number of parameters that can be adjusted in the process. In this work, a data-driven methodology was developed for automatic assessment of the impact of parameters such as the formulation and liquid-to-solid ratio in the slurry, and the gap used for its coating on the current collector, on the electrodes heterogeneity. A dataset generated by experimental measurements was used for training and testing a Machine Learning (ML) classifier namely Gaussian Naives Bayes algorithm, for predicting if an electrode is homogeneous or heterogeneous depending on the manufacturing parameters. Lastly, through a 2D representation, the impact of the manufacturing parameters on the electrode heterogeneity was assessed in detail, paving the way towards a powerful tool for the optimization of next generation of battery electrodes.http://www.sciencedirect.com/science/article/pii/S2666546821000446Machine learningElectrode manufacturingBatteryHeterogeneityData analysis |
| spellingShingle | Marc Duquesnoy Iker Boyano Larraitz Ganborena Pablo Cereijo Elixabete Ayerbe Alejandro A. Franco Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity Energy and AI Machine learning Electrode manufacturing Battery Heterogeneity Data analysis |
| title | Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| title_full | Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| title_fullStr | Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| title_full_unstemmed | Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| title_short | Machine learning-based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| title_sort | machine learning based assessment of the impact of the manufacturing process on battery electrode heterogeneity |
| topic | Machine learning Electrode manufacturing Battery Heterogeneity Data analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2666546821000446 |
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