Electrochemical performance enhancement of Sodium-Ion batteries fabricated with NaNi1/3Mn1/3Co1/3O2 cathodes using support vector Regression-Simplex algorithm approach

Abstract

Sodium-ion batteries have low energy density, low capacity, and inferior cycling performance when compared with Li-ion batteries. However, lithium depletion poses a serious problem for the production and cost of Li-ion batteries. In the present work, NaNi1/3Mn1/3Co1/3O2 was synthesized as the cathode material for Na-ion batteries using the sol–gel method. The conventional cathode material used in Na-ion batteries had been replaced with the synthesized cathode material, and the data had been collected by performing charging/discharging experiments. The support vector regression synchronized crossvalidation simplex algorithm cluster was then used for predictive modeling and optimization of the fabrication process of the positive electrode material of sodium-ion batteries. The stable normal distribution without any skewness validated the robustness of the model for better accuracy and stability of the Na-ion batteries. The optimized value of capacity is 176 mAh/g for 99 cycles, which is better than those of conventional batteries used for commercial storage purposes