COMPUTATIONAL MODELING AND OPTIMIZATION OF SODIUM-IRON BATTERY MATERIALS FOR ENHANCED PERFORMANCE

Abstract

Due to the abundance of sodium and its low cost, sodium-ion batteries (SIBs) are receiving a lot of interest as an alternative to lithium-ion batteries. Sodium-iron (Na-Fe) batteries are just one of the many SIB technologies and they have the following advantages; superior cycle stability, thermal stability, and low cost. Nevertheless, there are still difficulties with low energy density, ineffective rate performance, and voltage variability. This paper investigates how computational modeling can be used to optimize the electrochemical performance of sodium-iron battery materials. A Density Functional Theory (DFT), Molecular Dynamics (MD) simulations and Finite Element Analysis (FEA) were combined to explore the characteristics of various sodium-iron materials, such as NaFePO 4, NaFeSO 4, and NaFeO 2. Primary performance measures (ionic conductivity, voltage stability, energy density and cycle life) were modeled and genetic optimization applied to optimize material. The findings indicate a great enhancement in energy density and cycle stability, with NaFePO 4 demonstrating a 16.67 per cent rise in energy density, and NaFeSO 4 displaying better ionic conductivity. This paper illustrates how computational methods can be used to speed up the process of designing high-performance sodium-iron batteries. The results are beneficial in developing and commercializing sodium-ion technologies in the future in large-scale application to energy storage.