The ability to predict lithium-ion battery (LIB) degradation is worth billions to the global automotive, aviation and energy storage industries, as it enables them to improve performance and safety and reduce warranty liabilities. Many different degradation mechanisms occur inside LIBs, none of whom can be measured directly during operation. Their consequences can be ‘observed’ as capacity and power fade, but are not sufficient to form a reliable prediction. In contrast, computer simulations allow access to internal states of the battery and track the evolution of each relevant degradation mechanism. Computer simulations from a trustworthy model also enable predictions in a quick and cost-effective manner, compared to experiments.

As degradation in a real battery involves multiple mechanisms that are strongly coupled with one another, trustworthy predictions can only be made from models that account for these multiple interactions. While many models of LIB degradation exist, only few consider more than two degradation mechanisms, and even fewer consider direct interactions between the mechanisms.

In this work, we report the first attempt to directly couple the multiple degradation mechanisms occurring in the negative electrode, enabling us to map the different pathways through the complex and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open-source Python-based 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 around €250000 experimentally. Crucially, the model enables one to directly observe the consequences of the different paths of degradation on the physical consequences of loss of lithium inventory and loss of active material. For a single cell type, we demonstrate that there are at least five different pathways to reach end-of-life, depending on how the cell is used. While parametrization of the degradation models remains a major challenge, the model enables cell and battery designers to target a particular usage pattern and extend battery life.