Lithium-ion battery degradation: how to model it

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Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. Many different degradation mechanisms occur inside LIBs and none of them can be measured directly during operation, only their consequences can be ‘observed’ but are normally lumped into capacity and power fade. In contrast, computer simulations allow us to see ‘inside’ the simulated battery and track how much of each degradation mechanism occurs. Many such models exist, but few consider more than two mechanisms at a time, and even fewer consider direct interactions between the mechanisms. Yet, degradation in a real battery involves multiple mechanisms that are strongly coupled with each other.

In this work, we report the first published attempt to directly couple more than two degradation mechanisms in the negative electrode, and map different pathways through the complicated path dependent and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open source modelling environment uniquely developed to allow new physics to be implemented and explored quickly and easily. Crucially it is possible to see `inside’ the model and observe the consequences of the different patterns of degradation, such as loss of lithium inventory and loss of active material. For the same cell we have already uncovered five different pathways that can result in end-of-life, depending on how the cell is used. Such information would enable a product designer to either extend life or predict life based upon the usage pattern. However, parameterization of the degradation models remains as a major challenge, and requires the attention of the international battery community.

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