Lithium sulfur (LiS) batteries are promising for next-generation energy storage. Due to their high energy density, lithium sulfur batteries are attractive for applications like electric vehicles and electric flights where weight is an important constraint. However, LiS batteries suffer from low coulombic efficiency, poor cycle life, and self-discharge. 1 Physics-based models can give insight into the internal states of the battery, which can be correlated to the mechanisms that contribute to performance degradation under various operating conditions. The first one-dimensional electrochemical discharge model for LiS batteries was developed by Kumaresan et al. using porous electrode theory, and considers transport, kinetics, thermodynamics, and morphological changes.2 The LiS chemistry includes multiple charge carriers and morphology changes as discharge proceeds, which contributes to the complexity and challenges of accurate modeling. The corresponding physics-based model results in a complex and numerically stiff equation system with variables that change across orders of magnitude and require proper initialization. The mathematical complexity of such electrochemical models motivates the use of reformulation and simplification techniques in order to justify their use in estimation, control, and optimization. Even for fast reformulated models, the large number of kinetic and transport parameters resulting from the complex chemistry makes their parameterization a non-trivial task. Several groups have used lumped and one-dimensional models to simplify modeling and study specific characteristics, such as self-discharge, rate-dependence of precipitation, or charging.3–9 This work demonstrates a mass-conserving tank-in-series model based on the original model developed by Kumaresan et al.2 The tank-in-series model is developed through volume-averaging of the model equations. The volume averaging results in overall balance equations governing the evolution of average variables in each region of the cell. The elimination of the spatial dependence from model equations substantially increases computational efficiency and reduces the number of parameters to be estimated. Comparisons of the tank-model simulations with the full model indicate the ability of the averaged model to capture discharge behavior at low to intermediate discharge rates, with deviations mainly observed towards the end of discharge. Other advantages and limitations of this model as compared to the full model are characterized in terms of electrochemical variable predictions, with applications in control and estimation for hybrid electric flights. The model predictions are compared against experimental data from 19.5 Ah pouch cells and have been improved through estimation of parameters. Acknowledgments The authors are thankful for financial support from BAE Systems and the Joint Center for Aerospace Technology Innovation. References X. Zhang, H. Xie, C.-S. Kim, K. Zaghib, A. Mauger, and C. M. Julien, Mater. Sci. Eng. R Reports, 121, 1 (2017).K. Kumaresan, Y. Mikhaylik, and R. E. White, J. Electrochem. Soc., 155, A576 (2008).Y. V. Mikhaylik and J. R. Akridge, J. Electrochem. Soc., 151, A1969 (2004).K. Yoo, M.-K. Song, E. J. Cairns, and P. Dutta, Electrochim. Acta, 213, 174 (2016).Y. X. Ren, T. S. Zhao, M. Liu, P. Tan, and Y. K. Zeng, J. Power Sources, 336, 115 (2016).T. Zhang, M. Marinescu, L. O’Neill, M. Wild, and G. Offer, Phys. Chem. Chem. Phys., 17, 22581 (2015).M. Marinescu, T. Zhang, and G. J. Offer, Phys. Chem. Chem. Phys., 18, 584 (2016).D. N. Fronczek and W. G. Bessler, J. Power Sources, 244, 183 (2013).A. F. Hofmann, D. N. Fronczek, and W. G. Bessler, J. Power Sources, 259, 300 (2014).
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