Model-based screening of electrode designs for charge and discharge


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The performance characteristic and energy density of lithium-ion batteries are significantly affected by the chosen cell design. The trend toward higher areal mass loadings enables an increase of the volumetric energy density of a cell due to a higher active to passive material ratio and a potential cost reduction due to a higher production throughput of theoretical electrode capacity. However, transport limitations within thick electrodes become increasingly relevant, limiting their C-rate capability. This directs the focus to structuring concepts like laser perforation or multilayered electrodes.
Research on optimal electrode design covers a wide range of both experimental and model-based approaches. As a result of the diversity of optimization objectives and benchmark systems, the practical utilization of this valuable knowledge seems difficult. Furthermore, electrode design studies commonly focus on discharge characteristics despite the increasing interest in fast charging.
To address this open challenge, we performed a broad model-based full cell parameter screening, using the physicochemical battery model by Doyle et al. that was extended for two-layered electrodes. The study investigates high-capacity electrodes for four modes of operation: charge and discharge at 0.1C and 1C, respectively. The results reveal a substantial difference in the identified best cell configurations at 0.1C and 1C. More importantly, the performance of the best cell designs for discharge at 1C and charge at 1C also differ significantly. This commonly neglected fact should be considered for the design of overall well performing cells. Moreover, the results highlight that lithium plating as a termination criterion during charge further increases the performance divergence between charge- and discharge-focused cell designs.
Considering the limitation of expensive experimental studies to significantly less than the roughly 5000 evaluated cell configurations in this simulation study, the best performing cell configuration may not be found. This highlights the importance of a joint experimental and model-based approach toward cell design optimization.

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