A New Modeling Approach to Simulate the SEI Layer Growth in Lithium-Ion Batteries to Predict Capacity Fade

Abstract

Lithium-ion batteries degrade as they are cycled due to different phenomena that occur inside the cell that lead to the loss of their capacity. One of the dominant mechanisms that leads to capacity fade is the growth of the solid-electrolyte interphase (SEI) layer due to the reduction of the electrolyte solvent on the electrode surface 1. This passive layer ideally prevents the electrolyte from interacting with the electrode, preventing further reduction of the electrolyte. However, the growth of the SEI layer during cycling leads to the irreversible loss of lithium available for cycling and increases the resistance of the battery.Physics-based models to study the growth of the SEI layer for predicting capacity fade are reported in the literature.2 -8 Adding the SEI layer growth mechanism to battery models can help predict the capacity fade of a cell under different charging protocols and drive cycles for EV and PHEV batteries 2. The different models that exist in literature consider the growth to be kinetic-limited 3 , 4 , diffusion-limited 5, or a combination of both 6 , 7 , 8. In previous studies, the relationship between the growth of the SEI layer and the interface resistance is assumed to be linear and the contribution of the same to the overpotential of the intercalation reaction is assumed to be ohmic. This is because the transport of lithium ions through the SEI layer is not included in these models.In this work, we propose a model that describes the growth of the SEI layer on the graphite anode as a moving interface. The mass transport of both lithium ions and the solvent in the electrolyte are modeled which are affected by this moving interface. The transport during kinetic-limited and diffusion-limited growth are analyzed for different C-rates as the battery is cycled. The ion transport also induces changes in the conductivity across the SEI layer which affects the potential that arises due to its growth. This important effect and its influence on capacity fade is studied by simulating battery cycling using the proposed model. These studies help improve the fundamental understanding of the impact of different battery design parameters and operating conditions on capacity loss and cell lifetime. Acknowledgement This work at the University of Texas at Austin was supported by U.S. DOE Office of Electricity award DEAC05-76RL01830 through PNNL subcontract 475525. References P. Arora, R. E. White, and M. Doyle, J. Electrochem. Soc., 145, 3647–3667 (1998).M. T. Lawder, P. W. C. Northrop, and V. R. Subramanian, J. Electrochem. Soc., 161, A2099–A2108 (2014).P. Ramadass, B. Haran, R. White, and B. N. Popov, J. Power Sources, 123, 230–240 (2003).G. Ning and B. N. Popov, J. Electrochem. Soc., 151, 1584–1591 (2004).H. J. Ploehn, P. Ramadass, and R. E. White, J. Electrochem. Soc., 151, 456–462 (2004).M. Safari, M. Morcrette, A. Teyssot, and C. Delacourt, J. Electrochem. Soc., 156, 145–153 (2009).X. G. Yang, Y. Leng, G. Zhang, S. Ge, and C. Y. Wang, J. Power Sources, 360, 28–40 (2017).N. Kamyab, J. W. Weidner, and R. E. White, J. Electrochem. Soc., 166, A334–A341 (2019).