Functional Data-Driven Framework for Fast Forecasting of Electrode Slurry Rheology Simulated by Molecular Dynamics

Abstract

Computational modeling of the manufacturing process of Lithium-Ion Battery (LIB) composite electrodes based on mechanistic approaches, allows predicting the influence of manufacturing parameters on electrode properties. However, ensuring that the calculated properties match well with experimental data, is typically time and resources consuming In this work, we tackled this issue by proposing a functional data-driven framework combining Functional Principal Component Analysis and K-Nearest Neighbors algorithms. This aims first to recover the early numerical values of a mechanistic electrode manufacturing simulation to predict if the observable being calculated is prone to match or not, i.e screening step. In a second step it recovers additional numerical values of the ongoing mechanistic simulation iterations to predict the mechanistic simulation result, i.e forecasting step. We demonstrated this approach in context of LIB manufacturing through nonequilibrium molecular dynamics (NEMD) simulations, aiming to capture the rheological behavior of electrode slurries. We discuss in full details our novel methodology and we report that the expected mechanistic simulation results can be obtained 11 times faster with respect to running the complete mechanistic simulation, while being accurate enough from an experimental point of view, with a F 1 score equals to 0.90, and a R 2 score equals to 0.96 for the learnings validation. This paves the way towards a powerful tool to drastically reduce the utilization of computational resources while running mechanistic simulations of battery manufacturing electrodes.

Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities

“…Furthermore, ML is also used to predict the viscosity calculation result from the first numerical steps of a non-equilibrium MD simulation: This allows for performing the fitting process in a faster way as each CGMD model has a different set of FFs parameters values, and it is used in optimization algorithms that do not need to run until the end. [31] The ARTISTIC project demonstrated this slurry modeling approach for multiple AM chemistries, such as NMC111, LFP, graphite, and organic AM for SIBs. [30][31][32] ML has also been applied in the ARTISTIC project for the prediction of the impact of slurry formulation, solid content, and viscosity (determined by the coater comma-gap and coating speed) on the loading and porosity of the fabricated electrodes (the drying rate kept constant).…”

“…[31] The ARTISTIC project demonstrated this slurry modeling approach for multiple AM chemistries, such as NMC111, LFP, graphite, and organic AM for SIBs. [30][31][32] ML has also been applied in the ARTISTIC project for the prediction of the impact of slurry formulation, solid content, and viscosity (determined by the coater comma-gap and coating speed) on the loading and porosity of the fabricated electrodes (the drying rate kept constant). [33] For this purpose, an experimental data set (ca.…”