(Digital Presentation) An Efficient Modeling Framework for Electrodeposition in Lithium Metal Batteries

Abstract

Lithium metal batteries are drawing attention as a next generation of lithium-ion batteries due to their advantage of high theoretical capacity. However, the widespread application and commercialization of this potential battery technology is not achieved yet due to the poor cyclability and safety issues. Despite the recent progress1-2 and development of numerous strategies for the long and durable cyclability1-4, the analysis of lithium metal evolution still depends on the experimental approach in macroscale or molecular simulation in nanoscale5. In order to understand and optimize the system-level response of these lithium metal batteries, an accurate and robust modeling and simulation framework with a coupled macro and nanoscale approach is essential. In the previous reports, we have discussed simple 1D model to study stripping and plating of the lithium metal electrode and obtaining the characteristic inverse signatures in the cell voltage6, and appropriate boundary conditions ensuring mass conservation in a 2D model7.In this work, a 2D separator domain with initial surface morphology at the lithium metal electrode is considered for diffusion and migration of the lithium-ions in the domain along with reaction kinetics at the surface. The model is based on the mass and charge conservation in the system that captures the morphological evolution at the lithium metal electrode along the charge/discharge of the battery during cycling. This model is solved with in-house schemes based on spatial, temporal discretization schemes and coordinate transformation. The results show that the local current distribution at the electrode surface affects the rate and shape of the growth of the lithium metal at the negative electrode. The study has been performed for a wide range of geometric, kinetic and transport parameters. The proposed in-house model efficiently reaches a converged results compared with other numerical schemes both in speed and accuracy, and easily adaptive to optimizing tools towards cell design. Acknowledgments This research was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the US Department of Energy (DoE) through the Advanced Battery Materials Research Program (Battery500 Consortium). References K. N. Wood, E. Kazyak, A. F. Chadwick, K. H. Chen, J. G. Zhang, K. Thornton, and N. P. Dasgupta, ACS Cent. Sci., 2, 790-801 (2016).A. Pei, G. Zheng, F. Shi, Y. Li, and Y. Cui., Nano Lett., 17(2), 1132-1139 (2017).J. Liu, Z. Bao, Y. Cui, E. Dufek, J. B. Goodenough, P. Khalifah, Q. Li, B. Liaw, P. Liu, A. Manthiram, Y. S. Meng, V. R. Subramanian, M. F. Toney, V. V. Viswanathan, M. S. Whittingham, J. Xiao, W. Xu, J. Yang, X. Yang, and J. Zhang, Nat. Nanotechnol., 14 180-186 (2019).Y. Chen, Z. Yu, P. Rudnicki, H. Gong, Z. Huang, S. Kim, J. Lai, X. Kong, J. Qin, Y. Cui and Z. Bao, J. Am. Chem. Soc., 143(44), 18703-18713 (2021)S. Angarita-Gomez and P. B. Balbuena, Phys. Chem. Chem. Phys., 22, 21369-21382, (2020)M. Uppaluri, A. Subramaniam, L. Mishra, V. Viswanathan, and V. R. Subramanian, J. Electrochem. Soc., 167, 160547 (2020).L. Mishra, A. Subramaniam, T. Jang, K. Shah, M. Uppaluri, S. A. Roberts and V. R. Subramanian, J. Electrochem. Soc., 168, 092502 (2021).