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

ABSTRACT: 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. These datasets were used to accurately predict the voltage profiles from the arbitrary inputs of various galvanostatic charge-discharge schedules. The results demonstrated high prediction accuracy, with the coefficient of determination scores reaching 0.98 and 0.97 for test datasets obtained

SUBMITTER: Oka K 

PROVIDER: S-EPMC11582594 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature