(Invited) Accelerating Optimization of Lithium Ion Battery Manufacturing Via Multiscale Computations

Abstract

Electrochemical performance and lifetime of Lithium Ion Battery (LIB) composite electrodes strongly depend on their manufacturing parameters. Such parameters include the slurry formulation (i.e. the chemistry and amount of active material, carbon, binder and solvent), the temperature used to evaporate the solvent, the pressure applied during the electrode calendering and the time for the electrolyte impregnation. Within the perspective of developing more efficient LIBs at minimal R&D cost, it is important to develop robust tools able to predict the influence of manufacturing parameters on the electrodes mesostructure and the overall cell performance. In this keynote, i discuss the latest research progress in this sense within the ARTISTIC project, funded by the European Research Council (ERC). This project aims at developing and demonstrating a multiscale computational platform able to simulate the LIB manufacturing process and to predict optimal manufacturing parameters of LIBs responding to specific targets (energy density, etc.). Coarse Grained Molecular Dynamics, Discrete Element Method, Lattice Boltzmann and Continuum computational models are combined with deep learning algorithms to reach this objective. Examples of applications of this platform for NMC and graphite-based electrodes are discussed, in strong link with in house experimental characterizations. Physicochemical mechanisms leading to the composite electrode self-organization are discussed and guidelines towards the optimization of the multiple process steps are presented. At the end of the lecture, an experience using an in house Virtual Reality tool, to assess the manufacturing modeling results, will be proposed to the attendees. [1] ARTISTIC project (European Research Council Consolidator Grant #772873): https://www.u-picardie.fr/erc-artistic/[2] Franco, A. A., Rucci, A., Brandell, D., Frayret, C., Gaberscek, M., Jankowski, P., & Johansson, P. (2019). Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality?. Chemical Reviews. DOI: 10.1021/acs.chemrev.8b00239 [3] Rucci, A., Ngandjong, A. C., Primo, E., Maiza, M., & Franco, A.A., submitted (2019). [4] Ngandjong, A. C., Rucci, A., Maiza, M., Shukla, G., Vazquez-Arenas, J., & Franco, A. A. (2017). Multiscale Simulation Platform Linking Lithium Ion Battery Electrode Fabrication Process with Performance at the Cell Level. The journal of physical chemistry letters, 8(23), 5966-5972. Figure 1