Abstract
The initial efficiency is a very important criterion for carbon anode material of Li-ion battery. The relationship between initial efficiency and structure parameters of carbon anode material of Li-ion battery was investigated by an artificial intelligence approach called Random Forests using D10, D50, D90, BET specific surface area and TP density as inputs, initial efficiency as output. The results give good classification performance with 91% accuracy. The variable importance analysis results show the impact of 5 variables on the initial efficiency descends in the order of D90, TP density, BET specific surface area, D50 and D10; smaller D90 and larger TP density have positive impact on initial efficiency. The contribution of BET specific surface area on classification is only 18.74%, which indicates the shortcoming of BET specific surface area as a widely used parameter for initial efficiency evaluation.
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