Lithium-ion battery degradation: how to model it

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. However, there are no reliable models and developers must resort to expensive and time consuming experiments, and only a few can afford to test under every realistic combination of operating conditions. In this paper, 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. We have run 30 simulations of 1000 cycles, which would have cost ~$300000 experimentally. 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.