Data-driven analysis of temporal evolution of battery slurry in pipe systems

Lithium -ion batteries (LIBs) are considered one of the primary energy storage systems, with their electrodes playing a crucial role in battery performance. This study analyzes temporal evolution of battery anode slurry during transportation, which can result in the manufacturing of defective products, and presents an in -situ change detection methodology. From a laboratory -scale pipe system, pressure and flowrate signals are recorded during five-day transportation experiments. Considering the system's periodicity, the Short -Time Fourier Transform (STFT) is adopted to utilize both time and frequency information. Using STFT-processed data, we train a Convolutional Neural Network (CNN) classifier and successfully detect temporal variations in the transportation signals. Furthermore, through Gradient Class Activation Map (Grad -CAM) technique, distinguishing patterns for each classified data are verified. Concurrently, the slurry's rheological properties measured through daily sampling consistently exhibit gradual changes during transportation. Although we apply an arbitrary daily label as criteria of variations, hypothesizing that slurry's microstructure and subsequently rheological properties and measurement signals change over time during transportation, an accurate detection is achieved, even if there are nearly imperceptible differences in the signal data to the naked eye. This study proposes a promising methodology capable of capturing the microstructure and rheological evolution of slurries without any rheological measurements.