  Early degradation prediction of lithium-ion batteries is crucial for ensuring
safety and preventing unexpected failure in manufacturing and diagnostic
processes. Long-term capacity trajectory predictions can fail due to cumulative
errors and noise. To address this issue, this study proposes a data-centric
method that uses early single-cycle data to predict the capacity degradation
trajectory of lithium-ion cells. The method involves predicting a few knots at
specific retention levels using a deep learning-based model and interpolating
them to reconstruct the trajectory. Two approaches are used to identify the
retention levels of two to four knots: uniformly dividing the retention up to
the end of life and finding optimal locations using Bayesian optimization. The
proposed model is validated with experimental data from 169 cells using
five-fold cross-validation. The results show that mean absolute percentage
errors in trajectory prediction are less than 1.60% for all cases of knots. By
predicting only the cycle numbers of at least two knots based on early
single-cycle charge and discharge data, the model can directly estimate the
overall capacity degradation trajectory. Further experiments suggest using
three-cycle input data to achieve robust and efficient predictions, even in the
presence of noise. The method is then applied to predict various shapes of
capacity degradation patterns using additional experimental data from 82 cells.
The study demonstrates that collecting only the cycle information of a few
knots during model training and a few early cycle data points for predictions
is sufficient for predicting capacity degradation. This can help establish
appropriate warranties or replacement cycles in battery manufacturing and
diagnosis processes.
