  Capacity degradation of lithium-ion batteries under long-term cyclic aging is
modelled via a flexible sigmoidal-type regression set-up, where the regression
parameters can be interpreted. Different approaches known from the literature
are discussed and compared with the new proposal. Statistical procedures, such
as parameter estimation, confidence and prediction intervals are presented and
applied to real data. The long-term capacity degradation model may be applied
in second-life scenarios of batteries. Using some prior information or training
data on the complete degradation path, the model can be fitted satisfactorily
even if only short-term degradation data is available. The training data may
arise from a single battery.
