Correlation function of specific capacity and electrical conductivity on carbon materials by multivariate analysis

An attention to a Li ion battery for electric vehicles has been attracted, but there are two huge problems: a short mileage and slow charging speed. Therefore, it is required to improve the specific capacity and electrical conductivity of the carbon material used for an anode and a conductive agent. To solve these problems, this study organized correlation analysis with descriptor vectors by collecting experimental properties including capacity and conductivity from 21 various types of carbon materials. Focusing on the flux of Li ion, it was found that the capacity was dependent on the intercalation of Li ions, which lead to propose the correlation equation based on the Hill equation. Furthermore, the intercalation occurred at the edge of basal plane lead an increase of the width of the gap between two graphene layers, followed by a diffusion through the basal plane, finally the expanded gap recovered its original width. Also, it was found that the variables which are sensitive to the conductivity are largely dependent on the defects and especially the number of graphene layers around the surface, which proposed a correlation equation that can predict the capacity and conductivity. To validate these functions, we checked the effectiveness of it with both experimental data from 27 previous studies and statistical method. As a result, it was confirmed enough to predict them. Finally, a candidate structure for improving the battery performance was proposed, thus our study aims to guide the exploration of electrode materials for LIBs.

Automated summary

This study investigates the factors affecting the specific capacity and electrical conductivity of carbon materials used in Li-ion batteries for electric vehicles. Correlation analysis and descriptor vectors were used to examine the intercalation of Li ions and the number of graphene layers on the surface, which were found to have significant impacts on the capacity and conductivity. The study proposes correlation equations based on these findings and validates their effectiveness using experimental data and statistical methods. A candidate structure for improving battery performance is also proposed.