Multiscale Computational Approaches for the Simulation of Battery Electrodes Fabrication

Abstract

The automotive lithium ion battery (LIB) industry has reached a point where optimization studies are vital to achieve high performance at low environmental and economic cost. Significant progress has been made in linking structural properties to ionic transport and cell capacity response using either computer-aided reconstruction of real electrode structure or modeling on built artificial structures [1-2]. However a more holistic approach by combining electrode composition with fabrication parameters is crucial to produce results coherent with experiment. Computational modeling constitutes a powerful tool to establish such an approach, but until now only very few theoretical efforts on this topic have been reported so far [3-4]. In this contribution we aim to mimic standard LIB electrode fabrication techniques through innovative multiscale modelling and numerical simulation [5]. With electrode composition as a control parameter [4,6], respective interactions at the mesoscale level between the active material, conductive material, solvent, binders and dispersants are being considered within an in house three-dimensional Monte Carlo (MC) simulation approach describing the slurry preparation and the solvent evaporation (Figure). The simulations provide insights into the self-organization mechanisms of materials during fabrication as function of the composition, particles size distribution and polymer binder lenght. More specifically, we discuss here the efficiency, advantages and disavantages of using two types of MC methods, namely Metropolis MC and Kinetic MC through the Variables Step Size Method [7-8], on predicting the pore size distributions, porosity and effective electronic conductivity of LIB electrodes. The potential of these simulations to lead to proposals of new and highly efficient fabrication techniques is discussed in view of the wide diversity of active material chemistries now emerging for LIB applications. [1] A. A. Franco, RSC Adv., 3(2013) 13027. [2] Stephen E. Trask, Yan Li, Joseph J. Kubal, Martin Bettge, Bryant J. Polzin, Ye Zhu, Andrew N. Jansen, Daniel P. Abraham, J. Power Sources, 259(2014) 233. [3] Z. Liu, P.P. Mukherjee, Journal of The Electrochemical Society, 161 (8) (2014) E3248. [4] Z. Liu, V. Battaglia, P.P. Mukherjee, Langmuir, 30 (50) (2014) 15102. [5] Physical Multiscale Modeling and Numerical Simulation of Electrochemical Devices for Energy Conversion and Storage: From Theory to Engineering to Practice. A.A. Franco, M.L. Doublet, W.G. Bessler (Eds.), Springer (2015). [6] G. Liu, H. Zheng, X. Song, V. S. Battaglia J. Electrochem Soc., 159(3) (2012) A214. [7] M.A. Quiroga, A.A. Franco, J. Electrochem. Soc., 162 (7) (2015) E73. [8] G. Blanquer, Y. Yin, M.A. Quiroga, A.A. Franco, J. Electrochem. Soc., 163 (3) (2016) A329. Figure 1