A deep learning approach for solving diffusion-induced stress in large-deformed thin film electrodes. (July 2023) - Huang, He; Li, Yong; Xue, Yuan; Zhang, Kai; Yang, Fuqian
Abstract: Understanding the effects of large deformation on electrochemical performance of electrodes, such as silicon and tin, in lithium-ion battery has stimulated great interest in analyzing stress evolution during electrochemical cycling and in developing mechanochemical models for the stress analysis. As the complexity of mechanochemical models continues to increase, there is an urgent need to develop computational methods to reduce computational costs and increase computational efficiency. In this work, we propose a physics-inspired neural network (PINN) based on the DeepXDE deep learning library in analyzing diffusion-induced stresses (DIS) in a thin film electrode with large deformation, in which a loss function, which is associated with the loss functions of partial differential equations (PDEs) in the domain and the initial/boundary conditions for the mechanochemical problem, is introduced. Using the proposed physics-inspired neural network, the mechanical equations, including geometric, constitutive and equilibrium equations, in the framework of finite deformation theory as well as the mass transport equation are solved to obtain the stress evolution in the large-deformed thin-film electrode. The numerical results are in accord with the results obtained from finite element method. This work provides a unique approach for deep learning to solve the coupling mechanochemical problems in energy storage. Highlights: Coupling mechanochemical problems are solved by deep learning approach. A physics-inspired neural network for large-deformed electrode is proposed. Results predicted by neural network are in accord with the finite element solution.