In the domains of energy storage and electric mobility, sodium-ion batteries are undoubtedly becoming more and more well-liked. Electric vehicles using sodium-ion battery packs are not yet commercially available, so our research provides a potential construction that leads to commercialization as the next generation aims to replace lithium-ion batteries. 

The development of materials for sodium-ion batteries (electrodes, electrolytes, and separators) and their impact on the electrochemical performance of the cell have been the focus of a great deal of recent research on sodium-ion batteries. Because sodium-ion battery chemistry is comparable to that of lithium-ion battery systems, their development has progressed relatively quickly.  

Herein, this project focuses on synthesis novel materials with porous structure, high specific surface area and chemical stability as cathode and anode. For example, we aim to synthesis novel layered oxide composites with 2D and 3D structure as cathode materials while carbon composites as anode materials. Developing high energy density of Na-ion batteries relying on the low-cost and sustainable materials for large-scale energy storage beyond Li-ion technology through the optimization techniques in machine learning and density functional theory (DFT) simulation. 

• Develop low-cost and sustainable materials (cathode and anode using low Ni, Co content, friendly environmental resource) for Na-ion batteries. 

• Demonstrate the prototypes of full cell Na-ion batteries with energy density and cycle life which are comparable with Li-ion cell using graphite/LiFePO4 for large-scale application. 

• Establish Data-driven approach to study materials properties, to select appropriate materials for each component, and to optimize the synthesis and assembly process. 

• Elucidate the mechanism of Na-ion/electron/polaron migration in cathode material.

This research proposal outlines the following work packages to achieve the project objectives: 

WP1: Data-driven approach for materials and cell development (9 months) 

• Month 1-3: Data-driven approach with machine learning and computational techniques to enhance the development of low-cost and sustainable materials for Na-ion batteries.  

• Month 4-6: Identify critical correlations between material characteristics and battery performance by employing machine learning algorithms, statistical analyses, and data modeling. 

• Month 7-9: Utilize comprehensive data analysis to identify trends, anomalies, and critical factors influencing the performance of materials, facilitating informed decision-making in the material design and battery fabrication stages. 

WP2: Designing the green synthesis procedure (limited waste, recycling process or recycled materials) to develop low-cost and sustainable materials. (9 months) 

• Month 10-12: Select the bio-source (biomass based), abundant elements (Na, Mn, Fe, Al, Cu, Zn) based precursors. 

• Month 13-18: Develop synthesis protocol: precipitation and solid-state reaction (low toxic solvent, gas emission, recycle materials) 

• Month 19-21: Machine learning applies to optimize the selection of precursor materials and the synthesis process.  

WP3: Density Functional Theory (DFT) for fundamental material properties (6 months) 

• Month 22-24: Explore geometric structures of Na-ion ordering, electronic conductivity, and Na-ion migration using Density Functional Theory (DFT) method. 

• Month 25-27: Identify the oxidation/reduction stability of electrolyte through HOMO/LUMO values to understand the interfacial chemistry and battery degradation. 

WP4: Full cell design and assembly to deliver high-energy density Na-ion batteries 150-170 Wh/kg, 300 cycles at C/3 which is competitive with Li-ion batteries (LFP) (12 moths) 

• Month 28-30: Maximize cell energy level in considering different factors: electrode loading, electrode density, N/P ratio, E/C ratio, electrodes stacking, etc. 

• Month 31-33: Testing conditions: formation, cycling testing, deep of discharge, state of charge. Etc. 

• Month 34-36: Machine learning applies to optimize the coating electrode process and cell assembly conditions for achieving the highest specific capacity and long-life cycle of battery.