Many different degradation mechanisms occur in lithium-ion batteries, all of which interact with one another [1]. However, there are few fewer observable consequences of degradation than there are mechanisms [2]. It is possible to measure the different degradation modes: loss of lithium inventory (LLI), loss of active material (LAM), impedance change and stoichiometric drift [3].

It is not always possible to link these observable consequences of degradation to any particular mechanism or combination of mechanisms. Many models of degradation exist [4], but these models have many parameters that cannot be measured directly. A recent modelling study [5] found the number of parameters that the model is sensitive to is greater than the number of observable degradation modes.

However, the same model [5], despite including just four degradation mechanisms, found five possible degradation pathways a battery can follow. The model was built so that more mechanisms can easily be added later, so more pathways will be found.

In this work, a new approach to diagnosing battery degradation is proposed, based on these pathways. Experimental data for the degradation modes can be identified as being consistent with a particular pathway. Once the correct pathway is found, the parameters that particular pathway is sensitive to can be fit to the data, feeding back into the model.

[1] Jacqueline Edge et al., Phys. Chem.: Chem. Phys. vol. 23, pp. 8200-8221, 2021.

[2] Christoph Birkl et al., Journal of Power Sources vol. 341, pp. 373-386, 2017.

[3] Matthieu Dubarry et al., J. Electrochem. En. Conv. Stor. vol. 17, pp. 044701, 2020.

[4] Jorn Reniers et al., J. Electrochem. Soc. vol. 166 pp. A3189-A3200, 2019.

[5] Simon O’Kane et al., Phys. Chem.: Chem. Phys., submitted, 2022. https://arxiv.org/abs/2112.02037