ArXiv Preprint
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 is 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.
Pengfei Wen, Zhi-Sheng Ye, Yong Li, Shaowei Chen, Shuai Zhao
2023-01-02
