  For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion)
batteries, many models have been established to characterize their degradation
process. The existing empirical or physical models can reveal important
information regarding the degradation dynamics. However, there are no general
and flexible methods to fuse the information represented by those models.
Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical
or physical dynamic models with data-driven models. To take full advantage of
various information sources, we propose a model fusion scheme based on PINN. It
is implemented by developing a semi-empirical semi-physical Partial
Differential Equation (PDE) to model the degradation dynamics of Li-ion
batteries. When there is little prior knowledge about the dynamics, we leverage
the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying
governing dynamic models. The uncovered dynamics information is then fused with
that mined by the surrogate neural network in the PINN framework. Moreover, an
uncertainty-based adaptive weighting method is employed to balance the multiple
learning tasks when training the PINN. The proposed methods are verified on a
public dataset of Li-ion Phosphate (LFP)/graphite batteries.

Signal Processing;Artificial Intelligence;Machine Learning