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.

翻译：针对锂离子电池的预测与健康管理，已有众多模型被建立以刻画其退化过程。现有的经验或物理模型虽能揭示退化动力学的重要信息，但尚缺乏通用且灵活的方法来融合这些模型所蕴含的信息。物理信息神经网络作为一种高效工具，可融合经验或物理动力学模型与数据驱动模型。为充分利用多种信息源，我们提出一种基于PINN的模型融合方案，通过构建半经验-半物理的偏微分方程来模拟锂离子电池的退化动力学。在退化动力学先验知识匮乏时，采用数据驱动的深度隐物理模型自动发现底层控制动力学方程，并将所发现的动力学信息与PINN框架中替代神经网络挖掘的信息相融合。此外，训练过程中引入基于不确定性的自适应加权方法以平衡多学习任务。所提方法在磷酸铁锂/石墨电池公开数据集上得到验证。