Phenomenological Model of SEI Formation and Growth Leveraging Real-Time Expansion Measurements

Abstract

Every commercial lithium-ion battery undergoes formation at the end of the manufacturing process [1]. The formation process is time and capital-intensive, motivating battery manufacturers to develop new formation recipes to decrease formation time while preserving battery lifetime and safety [2]. Yet, despite its importance, a general framework for modeling formation of commercial lithium-ion battery systems is lacking, hindering the innovation cycle. While first-principles solid electrolyte interphase (SEI) formation modeling approaches exist [3], such models are often computationally expensive and challenging to validate, limiting their application towards predicting macroscopic variables relevant to commercial lithium-ion batteries such as capacity loss and resistance growth over life. Semi-physics based (i.e. semi-empirical, or phenomenological) approaches are popular [4], but such models are often parameterized using cell data collected after formation has already completed, making them unsuitable for capturing the initial stages of the SEI formation process.This work presents a framework for developing semi-physics based models of SEI formation and growth dynamics for use in commercial lithium-ion battery systems. The model enables real-time prediction of lithium consumption during the formation (i.e. first charge) process, which can be extended to track the lithium consumption over the entire lifespan of the cell. By explicitly considering the electrolyte reduction reaction dynamics during the first cycle, the model enables the study of how different formation protocols influence SEI passivation properties [5] and hence battery lifetime. The model leverages half-cell near-equilibrium potential curves to track the evolution of lithium stoichiometries and electrode potentials at each electrode, during and after the formation process. The model thus enables real-time tracking of the SEI-forming electrolyte reduction rates which can be used to estimate the quantity of lithium consumed to create the SEI. Model parameters are tuned against experimental electrode expansion measurements collected on NMC622|graphite pouch cells (Figure 1), which resolves both the reversible (i.e. lithiation-induced) and irreversible sources of electrode expansion [6],[7]. Model-predicted lithium consumption rates and electrode expansions are validated against an experimental dataset consisting of three different formation protocols which includes cycle life testing until the end of life.The modeling framework we propose enables practical pathways for bridging the electrochemistry of battery formation to macroscopic variables related to battery safety and lifetime, including total capacity loss, resistance growth, gas generation (during SEI and due to electrolyte oxidation at the positive electrode), and copper dissolution rates.[1] S. J. An, J. Li, C. Daniel, D. Mohanty, S. Nagpure, and D. L. Wood, “The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling,” Carbon N. Y., vol. 105, pp. 52–76, 2016.[2] Y. Liu, R. Zhang, J. Wang, and Y. Wang, “Current and future lithium-ion battery manufacturing,” iScience, vol. 24, p. 102332, Apr. 2021.[3] A. Wang, S. Kadam, H. Li, S. Shi, and Y. Qi, “Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries,” npj Computational Materials, vol. 4, no. 1, 2018.[4] L. von Kolzenberg, J. Stadler, J. Fath, M. Ecker, B. Horstmann, and A. Latz, “A four parameter model for the solid-electrolyte interphase to predict battery aging during operation,” J. Power Sources, vol. 539, p. 231560, Aug. 2022.[5] P. M. Attia, S. J. Harris, and W. C. Chueh, “Benefits of fast battery formation in a model system,” J. Electrochem. Soc., vol. 168, no. 5, p. 050543, 2021.[6] P. Mohtat, S. Lee, J. B. Siegel, and A. G. Stefanopoulou, “Towards better estimability of electrode-specific state of health: Decoding the cell expansion,” J. Power Sources, vol. 427, pp. 101–111, July 2019. Figure 1