Modeling and Analysis of Temperature-Dependent Capacity Degradation in Large Format Lithium-Ion Batteries

Abstract

The large-format lithium-ion batteries, widely used for electric vehicle applications, are generally characterized by spatial non-uniformities in temperature. The presence of thermal gradients and temperature-dependence of cell parameters are responsible for non-uniform degradation within the battery that leads to poor performance of the battery packs and also poses safety risks. In this work, we aim to develop a detailed physics-based simulation framework to model non-uniform, temperature-dependent capacity degradation within these large format batteries based on the classical pseudo two-dimensional (p2D) battery model. The electrochemical model will be coupled with degradation mechanisms to capture solid-electrolyte interface layer growth, reversible lithium plating, structural disintegration, and volume change within the electrodes on the cell level signatures and usable capacity over charge/discharge cycles. The effects of temperature inhomogeneities will be accounted in the multi-cell simulations at different operating temperatures for individual cells with dynamic currents and temperature and concentration dependent battery parameters as the model inputs.Overall, the primary focus of this study is to capture the effect of spatial temperature variation on battery capacity over cycles and help identify an improved design for the battery module. The model is implemented in an efficient equation-based form for the p2D model and degradation mechanisms with dynamic cell inputs using the mathematical modules in the finite element based solver, COMSOL Multiphysics. This approach enables an easier model development and analysis of the detailed physics and allows an easier implementation of any additional side reaction. The relative contributions of various fade modes have also been compared to understand their competitive interplay over cell cycles. The model predictions are validated with the available experimental data and in-house codes for each case. We believe that this simple yet detailed model can be further developed to be computationally feasible for adoption and implementation in a battery thermal management system.