Publication date: 22nd July 2026
Next-generation batteries, such as sodium-ion batteries (SIBs), offer a potentially cheaper and safer alternative to lithium-ion batteries as demand for energy storage continues to increase with the growth of renewable energy. However, current SIB technologies do not yet match the performance of lithium-ion batteries in key metrics such as energy density, cycle life and charging rates. Accelerating the pace of discovery is therefore essential for developing competitive technologies, but traditional research methodologies are often too slow to meet this need.
Self-driving laboratories combine automation and artificial intelligence to generate reproducible, statistically significant battery datasets that can guide future battery development. Factors such as operator experience, cell assembly procedures and attention to detail can strongly influence cell performance [1]. Although extensive datasets have been developed and utilised by researchers, these systems are typically designed to address highly specific problems, limiting standardisation and reducing the reusability of the resulting data [2, 3].
In this study, sodium-ion full cells were assembled and evaluated using three commercially available Na3V2(PO4)3 (NVP) cathode materials. These systems were systematically investigated using electrolyte formulations containing fluoroethylene carbonate (FEC) additive concentrations of 0.5, 1.25, 2.5 and 5 wt.%. The resulting dataset demonstrated that the commercial source of NVP had a substantial impact on cell performance. The poorest-performing NVP material showed the greatest improvement with the addition of FEC, whereas the highest-performing cells exhibited no measurable benefit from FEC addition in either rate capability or long-term cycling performance.
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