Prof. Dr. Alejandro A. Franco

Laboratoire de Réactivité et Chimie des Solides (LRCS), CNRS UMR 7314, Université de Picardie Jules Verne, Hub de l’Energie, Amiens, France
Réseau sur le Stockage Electrochimique de l'Energie (RS2E), Fédération de Recherche CNRS 3459, Hub de l’Energie, Amiens, France
ALISTORE-European Research Institute, Fédération de Recherche CNRS 3104, Hub de l’Energie, Amiens, France
Institut Universitaire de France, Paris, France

Abstract:

I discuss a digital twin of the manufacturing process of Lithium Ion Batteries (LIBs) we are developing within the context of the ARTISTIC project.¹ Such digital twin is supported on a hybrid approach encompassing a physics-based multiscale modeling workflow, machine learning models and high throughput experimental characterizations.^2-9 Different steps along the battery cells manufacturing process are simulated, such as the electrode slurry, coating, drying, calendering and electrolyte infiltration. The multiscale physical modeling workflow couples experimentally-validated Coarse Grained Molecular Dynamics, Discrete Element Method and Lattice Boltzmann simulations and it allows predicting the impact of the process parameters on the final electrode mesostructure in three dimensions. The predicted electrode mesostructures are injected in a continuum performance simulator capturing the influence of the pore networks and spatial location of carbon-binder within the electrodes on the electrochemical response. Machine learning models are used to accelerate the physical models’ parameterization, to unravel manufacturing parameters interdependencies from the physical models’ predictions and experimental data, and as a guideline for reverse engineering. The predictive capabilities of this digital twin, coupling physical models with machine learning models, are illustrated with results for different electrode formulations. Finally, data standardization challenges to ease the wide use of machine learning in the battery field are briefly discussed.^10-11

1. ERC Consolidator Project ARTISTIC, grant agreement #772873 (https://www.erc-artistic.eu/).
2. Shodiev, A., Primo, E., Arcelus, O., Chouchane, M., Osenberg, M., Hilger, A., Manke, I., Li, J., & Franco, A.A. (2021) Energy Storage Materials, 38, pp.80-92.
3. Ngandjong, A. C., Lombardo, T., Primo, E. N., Chouchane, M., Shodiev, A., Arcelus, O., & Franco, A. A. (2021). Journal of Power Sources, 485, 229320.
4. Cunha, R. P., Lombardo, T., Primo, E. N., & Franco, A. A. (2020). Batteries & Supercaps, 3(1), 60-67.
5. Duquesnoy, M., Lombardo, T., Chouchane, M., Primo, E. N., & Franco, A. A. (2020). Journal of Power Sources, 480, 229103.
6. Lombardo, T., Hoock, J. B., Primo, E. N., Ngandjong, A. C., Duquesnoy, M., & Franco, A. A. (2020). Batteries & Supercaps, 3(8), 721-730.
7. Shodiev, A., Primo, E. N., Chouchane, M., Lombardo, T., Ngandjong, A. C., Rucci, A., & Franco, A. A. (2020). Journal of Power Sources, 227871.
8. Chouchane, M., Rucci, A., Lombardo, T., Ngandjong, A. C., & Franco, A. A. (2019). Journal of Power Sources, 444, 227285.
9. Ngandjong, A.C., Rucci, A., Maiza M., Shukla, G., Vazquez-Arenas J., Franco, A.A., J. Phys. Chem. Lett., 8 (23) (2017) 5966.
10. El-Bousiydy, H., Lombardo, T. Primo, E.N., Duquesnoy, M., Morcrette, M., Johansson, P., Simon, P., Grimaud, A., & Franco, A.A. "What can text mining tell us about lithium‐ion battery researchers’ habits?." (2021) Batteries & Supercaps. https://doi.org/10.1002/batt.202000288
11. Mistry, A., Franco, A.A., Cooper, S.J., Roberts, S.A., & Viswanathan, V., 2021. ACS Energy Letters, 6, 1422.