Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics

The computational simulation of the manufacturing process of lithium-ion battery composite electrodes based on mechanistic models allows capturing the influence of manufacturing parameters on electrode properties.However,ensuring that these properties match with experimental data is typically computationally expensive.In this work,we tackled this costly procedure by proposing a functional data-driven framework,aiming first to retrieve the early numerical values calculated from a molecular dynamics simulation to predict if the observable being calculated is prone to match with our range of experimental values,and in a second step,recover additional values of the ongoing simulation to predict its final result.We demonstrated this approach in the context of the calculation of electrode slurries viscosities.We report that for various electrode chemistries,the expected mechanistic simulation results can be obtained 11 times faster with respect to the complete simulations,while being accurate with a R^(2)_(score) equals to 0.96.

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 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.