The economic potential of stationary storage systems can be strongly influenced by how battery ageing is accounted for in energy trading optimisation. State of the art trading algorithms typically rely on highly simplified degradation models that neglect the pronounced effect of certain stress factor such as depth of discharge (DoD) on capacity fade, resulting in unrealistic ageing cost estimations. Reusing automotive lithium ion cells (NMC111‖graphite) in 2nd-life applications can lower the initial investment and reduce the CO₂ footprint, but only if the remaining life of the reclaimed cells can be predicted with sufficient accuracy. The batteries investigated in this study originate from a compact SUV pack with an initial state of health of approximately 93 %.

To address this challenge a holistic ageing model was developed that couples a full electrical thermal cell model with a semi empiric degradation description. The electrical thermal model, built from electrical characterisation measurements (EIS, qOCV, and pulse tests), predicts voltage, current and temperature for any prescribed load profile and therefore provides the stress factors SOC, temperature, DoD and C-rate for the ageing simulation. Cyclic and calendar ageing tests were conducted. From each test the ageing behaviour was extracted and subsequently fitted with continuous functions, enabling interpolation and extrapolation to operating points not directly measured. The simulation workflow proceeds iteratively: a predefined load profile is fed into the electro thermal model, the resulting stress factors are used by the ageing model to update the cell’s electrical parameters, and the next cycle is simulated with the updated parameters. This loop is repeated a predefined number of times, thereby generating a time resolved prediction of capacity degradation for the specific profile.

Using this framework three load profile scenarios were analysed, each corresponding to approximately one, two or three full cycles per day, resulting in different ageing trajectories The simulations over a ten year horizon reveal that each additional full cycle per day accelerates capacity loss by roughly 2.5 percentage points, leading to a total difference of about five percentage points in capacity fade between the most and least aggressive scenarios. In conclusion, the presented holistic ageing model, based on the framework of Schmalstieg et al. [1], provides a stress aware prediction of degradation for 2nd-life batteries. By integrating realistic ageing costs into the decision making process, operators can optimise load profiles that maximise profit while extending battery lifetime and reducing environmental impact.

Reference
[1] J. Schmalstieg, S. Käbitz, M. Ecker, D. U. Sauer, “A holistic aging model for Li(NiMnCo)O₂ based 18650 lithium ion batteries,” Journal of Power Sources, vol. 257, pp. 325 334, 2014. DOI: https://doi.org/10.1016/j.jpowsour.2014.02.012