Hybrid nanophotonic-microfluidic sensor integrated with machine learning for operando state-of-charge monitoring in vanadium flow batteries

This research presents an advanced method for measuring State of Charge (SoC) in Vanadium Redox Flow Batteries (VRFB) using Refractive Index (RI) combined with Machine Learning (ML). The study comprised of three primary phases: ex-situ measurements, in-operando measurements, and ML model training. Initially, tests were conducted using a hybrid photonic-microfluidic sensor on simulated solutions mimicking specific VRFB SoCs. Subsequently, in-operando measurements were performed during cyclic processes within an experimental VRFB. Finally, utilizing the experimental data, an ML model was trained to accurately predict SoC by analyzing spectral characteristics. This study illustrates the potential of RI-based VRFB SoC measurement methods over the long term and addresses technological gaps by establishing a platform for precise SoC prediction with minimal cycle data.