Large-scale grid-connected batteries consist of various components, each of which influence the behaviour. Schimpe et al. [1] simulated the contributions of different components to the round-trip efficiency of a 192 kWh battery and found that the power electronics and the load profile have major impacts. The effect on the lifetime of the battery was not considered.Additionally, there are different configurations to connect the cells into modules and racks. Rumph et al. [2] used a multi-physics model to analyse the electrical and thermal behaviour of small modules of series and parallel connected cells. They showed how cell-to-cell variations affect the current distribution during peak currents and how matching cells could reduce the inhomogeneities. However, only short durations could be simulated due to the computational complexity of the models, excluding long-term effects such as degradation or system performance.Finally, Dechent et al. [3] used equivalent circuit models with parametric degradation models to study the degradation of large battery packs under various topologies and cell-to-cell variations. Monte Carlo simulations showed widely varying degradation trends for different systems. However, the coupling of the ageing model with the battery and thermal models was on a longer time scale, and combined with the non-physical nature of the models, this might introduce errors.In this work, SLIDE, a ‘virtual cycler’ physics-based degradation modelling framework for lithium-ion cells [4], is expanded to simulate the degradation of battery systems. Figure 1 shows the degradation trajectories of 10 cells with minor capacity variations grouped in one module during calendar ageing at 25 degrees and 90% state of charge. If all 10 cells are connected in series, small cell-to-cell differences result in a diverging degradation trend, which is not observed for parallel-connected cells. It should be noted that no balancing circuit was included, such that the diverging degradation is due to small differences in the voltages during rest. The smaller capacity cells will rest at higher potentials, accelerating the degradation and exacerbating the different degradation rates, which is prevented in parallel-connected cells.Figure 1 shows how the module configuration can affect the degradation of individual cells and small cell-to-cell variations. Once the model is extended to combine multiple modules, as well as adding thermal coupling, these effects will be studied in more detail.[1] M. Schimpe et al., “Energy efficiency evaluation of a stationary lithium-ion battery container storage system via electro-thermal modeling and detailed component analysis,” Appl. Energy, vol. 210, no. November 2017, pp. 211–229, 2018.[2] K. Rumpf, A. Rheinfeld, M. Schindler, J. Keil, T. Schua, and A. Jossen, “Influence of Cell-to-Cell Variations on the Inhomogeneity of Lithium-Ion Battery Modules,” J. Electrochem. Soc., vol. 165, no. 11, pp. A2587–A2607, Aug. 2018.[3] P. Dechent, I. Schoeneberger, and D. U. Husta, Friedrich Emanuel Sauer, “From Variability and Aging Rate Spread to Quantification of Failure Rates in Lithium-Ion Packs,” in MA2019-04 188, 2019.[4] J. M. Reniers, G. Mulder, and D. A. Howey, “Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 166, no. 14, pp. A3189–A3200, Sep. 2019. Figure 1