Electrochemical Performance Enhancement of Sodium-Ion Batteries Fabricated With NaNi_1/3Mn_1/3Co_1/3O₂ Cathodes Using Support Vector Regression-Simplex Algorithm Approach

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, NaNi_1/3Mn_1/3Co_1/3O₂ 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 cross-validation 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.