Recent studies emphasise the importance of assessing battery degradation under real operational conditions instead of under laboratory-controlled, often isolated, accelerated ageing conditions. Therefore, this thesis investigates degradation in a grid-scale LFP/graphite battery energy storage system (BESS) subjected to highly dynamic real-world load profiles. Degradation over two years was characterised by a 10% increase in internal resistance and a 9% loss in lithium inventory (LLI), through the first method of data-driven ageing analysis. Key indicators supporting this include internal resistance trends, quasi-constant voltage segments, Tafel-like slopes, and voltage relaxation behaviour.

Currently, BESS owners have to rely on annual warranty checks set by the BESS suppliers and checked by the suppliers through annual self-conducted capacity tests, while system owners often lack insight into internal degradation processes. This highlights the need for independent analysis and improved diagnostic tools.

To quantify degradation, both empirical equivalent circuit models (ECMs) and physics-based models (SPM, SPMe, DFN) were applied. SPMe and DFN show good agreement with measured voltage responses under constant power capacity test simulations. A SEI growth model was fitted to explain calendar ageing, reproducing the observed trends in resistance and LLI, and confirming the assumption of SEI growth as the dominant ageing mechanism. The ageing behaviour showed strong SOC dependence. This work demonstrates how combining data-driven qualitative analysis with empirical and electrochemical modelling enables tracking of cell-level degradation in real-world BESS.