  A latent function decomposition method is proposed for forecasting the
capacity of lithium-ion battery cells. The method uses the Multi-Output
Gaussian Process, a generative machine learning framework for multi-task and
transfer learning. The MCGP decomposes the available capacity trends from
multiple battery cells into latent functions. The latent functions are then
convolved over kernel smoothers to reconstruct and/or forecast capacity trends
of the battery cells. Besides the high prediction accuracy the proposed method
possesses, it provides uncertainty information for the predictions and captures
nontrivial cross-correlations between capacity trends of different battery
cells. These two merits make the proposed MCGP a very reliable and practical
solution for applications that use battery cell packs. The MCGP is derived and
compared to benchmark methods on an experimental lithium-ion battery cells
data. The results show the effectiveness of the proposed method.
