Electrical Network Structures in Lithium-Ion Batteries: Model-based Assessment of Uncertainty and Capacity Fade


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This poster presents a hybrid model approach that is applied to assess the effect of the electrical network structure on the electrochemical performance and degradation of a lithium-ion battery. Therefore, the novel model combines a classical electrochemical model based on porous electrode theory with an electrical network model. This enables to investigate of the influence of different electrical network structures on electrochemical performance. Fundamentally different generic network structures, i.e., homogenous, random, and scale-free networks, are generated and analyzed in terms of uncertainty, degradation, and failure. Here, degradation of the electrical network is modeled by edge removal, which represents the mechanical degradation of the CBM during battery utilization.
The results show that the cell performance and degradation depend on the respective network structure. Networks based on a power-law degree distribution, i.e. scale-free structures, show significant uncertainty of the electrical conductivity and cell performance, due to inhomogeneous current distributions. Such networks possess few highly connected nodes. These central nodes lead to high sensitivities against edge removal. In contrast, normally distributed connectivity, i.e. random networks, show a more homogeneous current distribution and thus less uncertainty. Moreover, comparing networks with the same initial performance, it can be seen, that random networks are significantly more robust against the removal of conducting pathways. In general, it is shown that highly deagglomerated and normally distributed connectivity is in theory favorable with respect to uncertainty and capacity fade.
To conclude, results indicate that CBM is not sufficiently evaluated by characterizing solely the bulk conductivity of the electrode. Instead, the detailed network structure should be considered complementary. The presented simulation approach enables a better mechanistic understanding of the CBM, which is the basis for a knowledge-based optimal design of electrode microstructures.

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