  Lithium-ion batteries degrade due to usage and exposure to environmental
conditions, which affects their capability to store energy and supply power.
Accurately predicting the capacity and power fade of lithium-ion battery cells
is challenging due to intrinsic manufacturing variances and coupled nonlinear
ageing mechanisms. In this paper, we propose a data-driven prognostics
framework to predict both capacity and power fade simultaneously with
multi-task learning. The model is able to predict the degradation trajectory of
both capacity and internal resistance together with knee-points and end-of-life
points accurately at early-life stage. The validation shows an average
percentage error of 2.37% and 1.24% for the prediction of capacity fade and
resistance rise, respectively. The model's ability to accurately predict the
degradation, facing capacity and resistance estimation errors, further
demonstrates the model's robustness and generalizability. Compared with
single-task learning models for forecasting capacity and power degradation, the
model shows a significant prediction accuracy improvement and computational
cost reduction. This work presents the highlights of multi-task learning in the
degradation prognostics for lithium-ion batteries.
