Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies
Authors: Lombardo Teo , Cunha Ricardo Pinto , Primo Emiliano , Franco Alejandro A. 
Published: May 2020

Nowadays, a great effort in terms of resources, time and money is dedicated to optimize Li-ion battery (LIB) manufacturing in order to improve the batteries performances. However, the number of parameters involved and the complexity of the physicochemical interactions throughout the associated manufacturing processes make highly difficult to find interdependencies between the final electrode characteristics and the fabrication parameters. The aim of the ERC founded ARTISTIC project[1] is to develop computational tools able to capture the effect of manufacturing processes on the final electrode’s electrochemical performance thanks to multiscale modelling[2–5] and Artificial Intelligence (AI). AI algorithms offer powerful capabilities to automatically identify correlations in very large datasets (big data) containing many variables[6–8]. In this presentation[9], different machine learning algorithms will be analyzed to find the best one in order to uncover the interdependencies between slurry manufacturing parameters and crucial properties of NMC-based cathodes, as their mass loading and porosity. The results revealed that the Support Vector Machine combines high accuracy and the possibility to predict the influence of manufacturing parameters in a straightforward graphical way. Thanks to this approach, several trends linking the electrode’s mass loading and porosity to the slurry’s main characteristics were disclosed and their validity demonstrated by in house experimental results. All of these trends were explained in terms of the viscoelastic behavior of the slurries.

It should be underlined that the proposed approach does not only considerably help to find interdependencies between LIB electrode manufacturing parameters, but it is designed in such a way to make it easily readable to both modelling and experimental researchers. The latter is a significant benefit of the proposed approach, because it could simplify the dialog between these two groups. Furthermore, the versatility of the classification variables that can be used to sort datasets paves the way towards exciting applications in the electrode engineering field, which is in desperate need of optimization tools for reducing costs and time related to the manufacturing process of LIBs.

Finally, a short perspective of the potential of AI methods to boost the discovery of ultra-high performance batteries (with particular reference to the European “Battery 2030+” initiative[10]) will conclude the talk.

References

[1] https://www.u-picardie.fr/erc-artistic/.

[2] A. Rucci, A. C. Ngandjong, E. N. Primo, M. Maiza, A. A. Franco, Electrochim. Acta
2019, 312, 168.

[3] A. A. Franco, A. Rucci, D. Brandell, C. Frayret, M. Gaberscek, P. Jankowski, P. Johansson, Chem. Rev.
2019, 119, 4569.

[4] A. C. Ngandjong, A. Rucci, M. Maiza, G. Shukla, J. Vazquez-Arenas, A. A. Franco, J. Phys. Chem. Lett.
2017, 8, 5966.

[5] M. Chouchane, A. Rucci, T. Lombardo, A. Ngandjong C., A. A. Franco, J. Power Source
2019.

[6] K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature
2018, 559, 547.

[7] A. F. de Almeida, R. Moreira, T. Rodrigues, Nat. Rev. Chem.
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[8] J. N. Wei, D. Duvenaud, A. Aspuru-Guzik, ACS Cent. Sci.
2016, 2, 725.

[9] R. P. Cunha, T. Lombardo, E. N. Primo, A. A. Franco, Batter. Supercaps
2019. Accepted

[10] https://battery2030.eu/.

Figure 1