Machine learning for optimal electrode wettability in lithium ion batteries
Electrode wetting is a critical step in the Lithium-Ion Battery manufacturing process. The injection of electrolyte in the electrodes’ porosity requires the application of pressure-vacuum pumping strategies without warranty that the full porosity will be fully occupied with electrolyte at the end of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of Power Sources Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666248523000069 |
| _version_ | 1797855266264317952 |
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| author | Amina El Malki Mark Asch Oier Arcelus Abbos Shodiev Jia Yu Alejandro A. Franco |
| author_facet | Amina El Malki Mark Asch Oier Arcelus Abbos Shodiev Jia Yu Alejandro A. Franco |
| author_sort | Amina El Malki |
| collection | DOAJ |
| description | Electrode wetting is a critical step in the Lithium-Ion Battery manufacturing process. The injection of electrolyte in the electrodes’ porosity requires the application of pressure-vacuum pumping strategies without warranty that the full porosity will be fully occupied with electrolyte at the end of this process step. The electrode wettability strongly depends on the contact angle between the electrolyte and the electrode, the electrode microstructure characterized by its porosity, pore network and tortuosity factor, the electrolyte viscosity and density. Computational fluid dynamics approaches such as the Lattice Boltzmann Method can provide relevant information of the filling process, yet these approaches come with significant computational cost. The use of machine learning techniques can provide surrogate models for the optimization of this multi-parameter process that depends on both chemical and physical properties. Within this context, we propose a general workflow for realizing this objective and provide detailed simulation-based experiments. These physics-informed surrogate models open the path to tractable, rapid solutions of parameter identification and design optimization problems. They also provide a general workflow for applications on other optimal battery material design problems. |
| first_indexed | 2024-04-09T20:19:49Z |
| format | Article |
| id | doaj.art-71ac927f50d74704b8de5fb8f8f4bba7 |
| institution | Directory Open Access Journal |
| issn | 2666-2485 |
| language | English |
| last_indexed | 2024-04-09T20:19:49Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Power Sources Advances |
| spelling | doaj.art-71ac927f50d74704b8de5fb8f8f4bba72023-03-31T05:55:30ZengElsevierJournal of Power Sources Advances2666-24852023-03-0120100114Machine learning for optimal electrode wettability in lithium ion batteriesAmina El Malki0Mark Asch1Oier Arcelus2Abbos Shodiev3Jia Yu4Alejandro A. Franco5Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France; Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, FranceLAMFA, CNRS UMR 7352, Université de Picardie Jules Verne, 33 rue Saint Leu, Amiens, 80039, France; Corresponding author.Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France; Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, FranceLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France; Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, FranceLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France; Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, FranceLaboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France; Réseau sur le Stockage Electrochimique de l’Energie (RS2E), FR CNRS 3459, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, 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, Paris, 75005, France; Corresponding author. Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l’Energie, 15 rue Baudelocque, Amiens Cedex, 80039, France.Electrode wetting is a critical step in the Lithium-Ion Battery manufacturing process. The injection of electrolyte in the electrodes’ porosity requires the application of pressure-vacuum pumping strategies without warranty that the full porosity will be fully occupied with electrolyte at the end of this process step. The electrode wettability strongly depends on the contact angle between the electrolyte and the electrode, the electrode microstructure characterized by its porosity, pore network and tortuosity factor, the electrolyte viscosity and density. Computational fluid dynamics approaches such as the Lattice Boltzmann Method can provide relevant information of the filling process, yet these approaches come with significant computational cost. The use of machine learning techniques can provide surrogate models for the optimization of this multi-parameter process that depends on both chemical and physical properties. Within this context, we propose a general workflow for realizing this objective and provide detailed simulation-based experiments. These physics-informed surrogate models open the path to tractable, rapid solutions of parameter identification and design optimization problems. They also provide a general workflow for applications on other optimal battery material design problems.http://www.sciencedirect.com/science/article/pii/S2666248523000069Lithium ion batteryElectrolyte wettabilityMachine learningLattice Boltzmann method |
| spellingShingle | Amina El Malki Mark Asch Oier Arcelus Abbos Shodiev Jia Yu Alejandro A. Franco Machine learning for optimal electrode wettability in lithium ion batteries Journal of Power Sources Advances Lithium ion battery Electrolyte wettability Machine learning Lattice Boltzmann method |
| title | Machine learning for optimal electrode wettability in lithium ion batteries |
| title_full | Machine learning for optimal electrode wettability in lithium ion batteries |
| title_fullStr | Machine learning for optimal electrode wettability in lithium ion batteries |
| title_full_unstemmed | Machine learning for optimal electrode wettability in lithium ion batteries |
| title_short | Machine learning for optimal electrode wettability in lithium ion batteries |
| title_sort | machine learning for optimal electrode wettability in lithium ion batteries |
| topic | Lithium ion battery Electrolyte wettability Machine learning Lattice Boltzmann method |
| url | http://www.sciencedirect.com/science/article/pii/S2666248523000069 |
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