The performance of lithium ion battery cells is influenced by microscopic and macroscopic parameters.  The microscopic parameters are generally responsible for the electrochemical, thermal, safety and lifetime performance of a battery. These parameters are cell chemistry, particle size, diffusion coefficient, equilibrium potential, reaction rate, thermodynamic parameters, thermal and electrical conductivity, heat capacity of active materials and different components of a cell etc. The macroscopic parameters are cell form, aspect ratio, thickness of active materials current collector and separator, tab size and its location. They are used in the cell design process and are optimized to enhance the performance and lifetime of a given cell.

When considering upscaling the cell size, the macroscopic parameters attain significance in determining the overall performance of large format cells, which may be largely ignored in short format cells.  Increase in the cell size has certain advantages such as, increased cell capacity, fewer connections to the battery pack (parallel connection), low assembly cost, high weight/volume ratio and high reliability of the components of a battery pack.  But on the other hand it increases the cell cost, increased safety risk and difficult thermal management etc. But the main disadvantages in terms of performance and lifetime are related to the inhomogeneities occurring in the cell due to increase in cell size. The inhomogeneities can be listed as temperature, current density and state of the charge (SOC) distribution. 

The current distribution in the cell is affected by tab configuration, current collector thickness and cell aspect ratio. Recent studies show that increasing the cell size increases inhomogeneous current density distribution. Despite the optimized design, the current flow near the tabs is constricted and the location near cell tabs experience higher current density compared to surfaces far away from the tabs. The current distribution influences the local heat generation and introduces temperature inhomogeneity, especially near cell tabs. It also introduces SOC inhomogeneity as SOC in the integral of current over time. These inhomogeneities are related to one another and will have a cascading effect on the overall cell performance.  A long term exposure to these inhomogeneities leads to localized aging of the cell. The localized aging creates stronger and weaker regions in cell and further accelerating the aging and leads to premature end of life (EOL) of the cell.

With the existing methodologies it is difficult to evaluate cell inhomogeneity as it is completely a localized process. However, recently some experimental and modelling techniques are able to evaluate cell inhomogeneity. These methods can be classified into two as, direct measurement such as, spatial temperature measurements both inside and on the cell surface, multiple or segmented electrode measurements, in-situ and ex-situ diffraction or neutron imaging tools and the second classification is modelling and simulation techniques, which is the focus of this study.

This paper shows two modelling techniques, first with spatially distributed battery model developed in Matlab/Simulink and second with Multiphysics based modelling in COMSOL.  Both models are able to evaluate inhomogeneity in a large format cell. This work also compares these modelling techniques with respect to complexity, computation and accuracy of the simulation results.