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.

As the energy transition gains momentum, the development of effective energy storage technologies is crucial. Among these technologies, batteries are of utmost importance as they store chemical energy that can be converted into electrical energy. The operation of batteries is a complex process that involves the interplay of material technology, multiphysics transport phenomena, and mechanical effects. While experiments can reveal new and unexpected features of batteries in various conditions, simulation models offer a costand time-effective way to gain valuable insights across a wider range. However, the accuracy and fidelity of mathematical models are directly proportional to the complexity of describing all the relevant phenomena. To date, equivalent-circuit models have been the dominant framework for industrial applications due to their simplicity and low computational cost. However, these models treat batteries as black boxes, which limit users’ ability to interpret the results. In contrast, physics-based models that couple electrochemistry, conservation laws, and heat equations can produce high-fidelity models that capture the intricacies of battery operation. The Multiphase Porous Electrode theory (MPET) provides a useful framework for integrating these phenomena and enables users to modify parameters that can significantly impact simulation results. In this study, experiments in different operating temperatures were conducted and analysed, and based on these outcomes, the accuracy and validity of MPET was tested. The root mean squared error between the simulation and experimental results was smaller than 5% in all cases. The correlation between the high temperature (50 ◦C) discharge curve and the ambient temperature discharge curve showed the high dependence of temperature to the state of charge of the battery which was confirmed by the experiments. Furthermore, a possible degradation mechanism could have an impact in the final results. The main research outcomes were the exponential relation between the temperature and the rate constant and between the particles conductivity and the temperature. Using these two relations, the model could reproduce the same trend and equal maximum capacity with the experiments. This shows the flexibility of the model in completely different operating conditions. After the validation, the active particle population model can be used to understand the coccurent or particle by particle intercalation and gives indentifications of hotspot in a battery. The final part was a sensitivity analysis about capacity optimization taking into account not only different C-rates but also different temperatures. Because the whole study was in an experimental coin cell, a relation to bigger battery systems should be built in the same manner using this software so as to facilitate the development of more effective energy storage technologies. Keywords: Li-ion batteries, phase separation materials, temperature dependency, parameter estimation, optimization, machine learning, physics-based models.