Discrete Element Method Simulation of Electrode Calendering and Its Impact on Electrochemical Performance
Authors: Ngandjong Alain C. , Lombardo Teo , N. Primo Emiliano , Chouchane Mehdi , Shodiev Abbos , Arcelus Oier , A. Franco Alejandro 
Published: July 2021

Lithium-ion battery (LIB) manufacturing optimization is crucial to reduce its CO2 fingerprint and cost, while improving their electrochemical performance. The latter is particularly challenging, due to the several parameters involved in LIB manufacturing and in their non-linear relationships. The aim of the ERC founded ARTISTIC project1,2 is to develop computational tools able to capture the effect of the manufacturing process on final electrode’s properties through multiscale modelling3–8 and machine learning (ML).9,10 We present here11,12 our latest work, applying discrete 3D (x,y,z) and continuum 4D (x,y,z and time) models to capture the effect of calendaring on LiNi0.33Mn0.33Co0.33O2–based cathodes. Our methodology is based on an experimentally validated Discrete Element Method model, which considers explicitly both active material (AM) and carbon-binder domain (CBD). The latter introduces important novelties respect the state of the art, such as: (i) the utilization of un-calendered electrode mesostructures resulting from experimentally validated simulations of the previous fabrication steps (slurry formulation and drying) in which the spatial distribution and interactions between AM and CBD particles are considered explicitly.3–5
(ii) the model validation through simultaneous comparison of experimental and simulated micro-indentation and porosity vs. calendering pressure curves. The resulting calendered electrodes were analyzed in terms of pore size distribution, tortuosity and particles arrangement. In addition, the evolution of the electrodes’ macroscopic electrochemical behavior (galvanostatic discharge curve and electrochemical impedance spectroscopy) upon the degree of calendering was discussed, offering added insights into the links between calendering pressure, electrode mesostructure and its electrochemical performance.

References

(1) https://www.u-picardie.fr/erc-artistic/.
(2) https://www.u-picardie.fr/erc-artistic/computational-portal/.
(3) Ngandjong, A. C.; Rucci, A.; Maiza, M.; Shukla, G.; Vazquez-Arenas, J.; Franco, A. A. Multiscale Simulation Platform Linking Lithium Ion Battery Electrode Fabrication Process with Performance at the Cell Level. J. Phys. Chem. Lett.
2017, 8 (23), 5966–5972. https://doi.org/10.1021/acs.jpclett.7b02647.
(4) Rucci, A.; Ngandjong, A. C.; Primo, E. N.; Maiza, M.; Franco, A. A. Tracking Variabilities in the Simulation of Lithium Ion Battery Electrode Fabrication and Its Impact on Electrochemical Performance. Electrochim. Acta
2019, 312, 168–178. https://doi.org/https://doi.org/10.1016/j.electacta.2019.04.110.
(5) Lombardo, T.; Hoock, J.; Primo, E.; Ngandjong, C.; Duquesnoy, M.; Franco, A. A. Accelerated Optimization Methods for Force-Field Parametrization in Battery Electrode Manufacturing Modeling. Batter. Supercaps
2020. https://doi.org/10.1002/batt.202000049.
(6) Chouchane, M.; Rucci, A.; Lombardo, T.; Ngandjong C., A.; Franco, A. A. Lithium Ion Battery Electrodes Predicted from Manufacturing Simulations: Assessing the Impact of the Carbon-Binder Spatial Location on the Electrochemical Performance. J. Power Sources
2019. https://doi.org/https://doi.org/10.1016/j.jpowsour.2019.227285.
(7) Chouchane, M.; Primo, E. N.; Franco, A. A. Mesoscale Effects in the Extraction of the Solid-State Lithium Diffusion Coefficient Values of Battery Active Materials: Physical Insights from 3D Modeling. J. Phys. Chem. Lett.
2020, 11 (7), 2775–2780. https://doi.org/10.1021/acs.jpclett.0c00517.
(8) Shodiev, A.; Primo, E. N.; Chouchane, M.; Lombardo, T.; Ngandjong, A. C.; Rucci, A.; Franco, A. A. 4D-Resolved Physical Model for Electrochemical Impedance Spectroscopy of Li(Ni1-x-YMnxCoy)O2-Based Cathodes in Symmetric Cells: Consequences in Tortuosity Calculations. J. Power Sources
2020, 454, 227871. https://doi.org/10.1016/j.jpowsour.2020.227871.
(9) Cunha, R. P.; Lombardo, T.; Primo, E. N.; Franco, A. A. Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies. Batter. Supercaps
2020, 3 (1), 60–67. https://doi.org/doi:10.1002/batt.201900135.
(10) Duquesnoy, M.; Lombardo, T.; Chouchane, M.; Primo, E. N. Data-Driven Assessment of Electrode Calendering Process by Combining Experimental Results , in Silico Mesostructures Generation and Machine Learning. J. Power Sources
2020, 480, 229103. https://doi.org/10.1016/j.jpowsour.2020.229103.
(11) Ngandjong, A.; Lombardo, T.; Primo, E.; Chouchane, M.; Shodiev, A.; Arcelus, O.; Franco, A. A. Investigating Electrode Calendering and Its Impact on Electrochemical Performance by Means of a New Discrete Element Method Model: Towards a Digital Twin of Li-Ion Battery Manufacturing. ChemRxiv
2020, 1–40.
(12) Ngandjong, A.; Lombardo, T.; Primo, E.; Chouchane, M.; Shodiev, A.; Arcelus, O.; Franco, A. A. Investigating Electrode Calendering and Its Impact on Electrochemical Performance by Means of a New Discrete Element Method Model: Towards a Digital Twin of Li-Ion Battery Manufacturing. J. Power Sources 2020, accepted .