Cell Performance Prediction of the Particle Structure of All-Solid-State Batteries By Numerical Simulation and Machine Learning

Abstract

All-Solid-State Battery (ASSB) is expected to be commercialized for electric vehicle applications. However, the correlation between the electrode structure and the cell performance is unknown, and no design theory has been established. Especially the effect of contact stress on solid-solid interface is important. And the ununiform reaction area cause the exsess current concentration. In our previous study, we developed various simulation model to understand the effect of heterogeneous structure and the effect of volume expansion on dynamic structure change during charging-discharging cycle from the viewpoint of durability. In this study, we developed new simulation model with stress calculation and electrochemical reaction, Moreover, the simulation results were used to create training dataset for machine learning. By using these approaches, we tried to develop new method to predict cell performance from composite electrode structure to cell performance.The stress estimation was performed using the discrete element method (DEM) [1]. The various material parameters are used such as, particle diameter (average and standard deviation), Young's modulus, Poisson's ratio and density. The distribution of the stresses on each particle was calculated from the DEM calculation results and confirmed to be approximately Gaussian.To simulate electrochemical reaction and mass transport in electrode layer, we used multi-network model (MNM)[6]. The schematic image of this model is shown in Fig.1, we used various network grid of active material network, solid electrolyte network and interface network. Basic equations of Li concentration, Li potential and electron potential were calculated in these networks. In this study, with DEM and MNM, we developed new simulation model shown in Fig.2.A convolutional neural network (CNN) [7] was used as the model for machine learning, and various structural parameters, including the DOD, cell potential, and stress on each particle calculated by DEM, were identified to create a dataset. 250 images were used for training, 150 of which were used for training, 50 for validation, and 50 for testing. The coefficient of determination (R²) of the standard deviation of DOD and potential is almost 0.7 in each condition, and the cell performance can be predicted by the machine learning model. The contact area of the electrochemical reaction interface, AM-SE, was calculated from the stress on the contact surface calculated by DEM and Hertzian contact theory [8]. Thus, we can express the relationship between interface mechanical stress on solid-solid and effective reaction, and this effect can be predicted with various structure of composite electrode layer by using machine learning. It was possible to predict the cell performance and effective contact area in the electrode layer of the all-solid-state battery.Acknowledgement:This research was carried out under the project (Grant Number JPMJMI21G4) supported by the JST-Mirai Program, Japan.