In battery-powered applications with high energy demands, the battery is one of the most expensive single components. Since electrochemical storages such as lithium-ion batteries are prone to a slow but steady degradation process, at some point the battery will not be able to fulfil the application's requirements any more. Due to the high component costs, predicting the battery's end-of-life is essential for economic operations. Generally, degradation models parametrised from laboratory ageing studies are the solution to predicting the batteries’ lifetime if the loads are known. In this work, we introduce a degradation modelling framework, which has been parametrised to depict the ageing influences of different cycle depths and state-of-charges for the calendar and cyclic degradation. It models the non-linear degradation trajectories, including variances, evaluated in a laboratory ageing study. The ageing framework deploys the degradation model to simulate the expected time, cycles and charge throughput until a given end-of-life criterion. The model and the ageing framework achieve good accuracy with almost zero verification error for the cyclic ageing degradation rate when using interval estimation with the ageing variance confidence intervals. For the point estimation, neglecting the cell's ageing variance, we achieved MAPE=10.88% and MAPE=22.97% for the degradation rates, respectively.