Deep learning based emulator for predicting voltage behaviour in lithium ion batteries.

This study presents a data-driven battery emulator using long short-term memory deep learning models to predict the charge-discharge behaviour of lithium-ion batteries (LIBs). This study aimed to reduce the economic costs and time associated with the fabrication of large-scale automotive prototype batteries by emulating their performance using smaller laboratory-produced batteries. Two types of datasets were targeted: simulation data from the Dualfoil model and experimental data from liquid-based LIBs.