Publications by authors named "Syuto Noguchi"
Article Synopsis
- The study developed a data-driven battery emulator using long short-term memory deep learning models to predict lithium-ion battery (LIB) charge-discharge behavior, aiming to reduce costs and time in creating automotive prototype batteries.
- It utilized simulation data from the Dualfoil model and experimental data from liquid-based LIBs to accurately forecast voltage profiles from various charge-discharge schedules, achieving high prediction accuracy (0.98 for simulations and 0.97 for experiments).
- The findings suggested that using just five training datasets could yield robust model performance, highlighting that machine learning can significantly speed up battery development and lower costs for large-scale production.
