Lithium-ion batteries (LIBs) are widespread with the fast development of new energy vehicles. The characteristics of LIB electrodes, including mass load, thickness, and porosity, are critical for battery performance such as energy density and lifespan. These characteristics are greatly influenced by the manufacturing methods and should be carefully considered during the production process development. However, the manufacturing process of electrodes is highly complex, involving a multitude of parameters. The traditional trial-and-error method has proven to be ineffective in improving manufacturing efficiency. In this study, we propose an artificial intelligence-based prediction method for estimating the key characteristics of electrodes. Specifically, it utilizes active material mass content, viscosity, solid-to-liquid ratio, and comma gap as input parameters. Compared to the traditional multiple linear regression method, the proposed method exhibits a significant improvement in accuracy. In certain cases, the root-mean-square error is reduced by an average of 35.5%, highlighting the superior prediction accuracy achieved by our method. Furthermore, we conduct a comparative analysis of different deep neural networks in predicting electrode characteristics. Finally, the importance of input features using the permutation feature importance analysis method is analyzed. By harnessing the powerful generalization ability of artificial intelligence, our method can be effectively applied to the manufacturing process of LIBs, resulting in a significant enhancement of battery production efficiency.

The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.