Electrode Heterogeneity Understanding through Data-Driven Benchmarking for Manufacturing Process Optimization
Authors: Duquesnoy Marc , Ayerbe Elixabete , Boyano Iker , A. Franco Alejandro 
Published: July 2021

Lithium-ion batteries (LIBs) are under the spotlight in automotive industry sector due to the very significant efforts made along the last 50 years to increase their performance and to reduce their cost [¹]. In that sense, the optimization of the battery production process needs to be carefully considered. Indeed, this process results from a complex interplay between numerous parameters, such as the slurry formulation, the drying rate of the solvent and the calendering pressure. The automatic assessment of the effect of those numerous parameters on the resulting electrodes properties is therefore crucially needed.

Existing computational tools started to capture such effect thanks to multiscale physical modeling [²,³] and Machine Learning (ML) analysis [⁴], which offer powerful capabilities for accelerating manufacturing process optimization which is currently based on trial and error.

The rheological properties of the electrode slurry and the coating process parameters constitute major factors affecting the quality of the produced electrodes in terms of degree of textural homogeneity, the latter directly affecting the LIB cell performance and cycle-life [⁵]. In this presentation, we introduce a novel data-driven study of the effect of electrode manufacturing parameters on the heterogeneity of NMC811-based cathodes. The proposed data-driven methodology is supported on experimental measurements, for defining automatically heterogeneous electrodes from mass loading and thickness properties, and analyzing their evolution within the batch of manufacturing parameters ranges, by using advanced statistics and Machine Learning.

This methodology pinpoints an easy way of optimizing LIBs manufacturing parameters for experimental researchers, complementing the ongoing efforts by us towards digital twins of battery manufacturing [⁶].

References

[1] Wentker, M., Greenwood, M., & Leker, J. (2019). A bottom-up approach to lithium-ion battery cost modeling with a focus on cathode active materials. Energies, 12(3), 504.

[2] Lombardo, T., Hoock, J. B., Primo, E. N., Ngandjong, A. C., Duquesnoy, M., & Franco, A. A. (2020). Accelerated Optimization Methods for Force‐Field Parametrization in Battery Electrode Manufacturing Modeling. Batteries & Supercaps. 3, 721 –730

[3] Chouchane, M., Rucci, A., Lombardo, T., Ngandjong, A. C., & Franco, A. A. (2019). Lithium ion battery electrodes predicted from manufacturing simulations: Assessing the impact of the carbon-binder spatial location on the electrochemical performance. Journal of Power Sources, 444, 227285.

[4] Pinto-Cunha, R., Lombardo, T., Primo, E., & Franco, A. A. (2020). Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies. Batteries & Supercaps. (2020) 3, 60 –67.

[5] Ouvrard, Guy, et al. Heterogeneous behaviour of the lithium battery composite electrode LiFePO4. Journal of Power Sources 229 (2013): 16-21.

[6] Duquesnoy, M., Lombardo, T., Chouchane, M., Primo, E. N., & Franco, A. A. (2020). Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning. Journal of Power Sources, 480, 229103.

Figure 1