Physics-Based Impedance Model of Lithium Sulfur Batteries

Abstract

Time domain physics-based models for lithium sulfur have been developed with mechanisms of the reaction cascade and precipitation reactions, allowing for a deeper understanding of internal states of the battery.1 Such models are able to capture voltage curves well during discharge. However, there have been challenges to reproduce charging curves accurately. It has been suggested that the charge and discharge reaction pathways are different,2 and/or precipitation rates and expressions to take into account numerical instability associated with dissolution when charging need to be improved.3,4 Since it is challenging for existing physics-based models to demonstrate macro-reversibility, we aim to see if there is evidence of micro-reversibility as impedance modeling would require these mechanistic models to work over much smaller changes in state-of-charge. Applying such models to the frequency domain can also give us detailed insight on proposed mechanisms and processes that occur across a larger range of timescales that are not available during a slow charge/discharge. Fronczek and Bessler5 have simulated electrochemical impedance spectroscopy (EIS) spectra based on mechanisms in the widely accepted one-dimensional model developed by Kumaresan et al..1 They are the first to demonstrate this with a physics-based modeling approach using a potential step in the time domain and performing FFT on the current relaxation to obtain the impedance. We aim to extend this further by comparing to experimental trends seen in the literature6,7 and explore qualitative features in the EIS spectra based on physical processes. We use a different computational approach by transforming the governing equations in the Kumaresan model to the frequency domain. We present this system of linearized ordinary differential equations and solve them in a manner consistent with numerical approaches demonstrated previously.8 Acknowledgement This work was supported by the Advanced Battery Material Research (BMR) Program (Battery 500 Consortium). References K. Kumaresan, Y. Mikhaylik, and R. E. White, J. Electrochem. Soc., 155, A576 (2008). Q. Wang et al., J. Electrochem. Soc., 162, A474–A478 (2015). K. Yoo, M.-K. Song, E. J. Cairns, and P. Dutta, Electrochimica Acta, 213, 174–185 (2016). N. Kamyab, P. T. Coman, S. K. Madi Reddy, S. Santhanagopalan, and R. E. White, J. Electrochem. Soc., 167, 130532 (2020). D. N. Fronczek and W. G. Bessler, Journal of Power Sources, 244, 183–188 (2013). S. Waluś, C. Barchasz, R. Bouchet, and F. Alloin, Electrochimica Acta, 359, 136944 (2020). Z. Deng et al., J. Electrochem. Soc., 160, A553–A558 (2013). M. Pathak et al., J. Electrochem. Soc., 165, A1324–A1337 (2018).