State-of-charge estimation and prediction by machine learning models using experimental dataset of lithium-ion batteries based on ionic liquid modified LiFSI electrolyte

M. Shaheer Akhtar

State-of-charge estimation and prediction by machine learning models using experimental dataset of lithium-ion batteries based on ionic liquid modified LiFSI electrolyte

M. Shaheer Akhtar

Graduate School of Intergrated Physical-AI

Research output: Contribution to journal › Journal article › peer-review

Original language	Korean
Journal	Materials Letters
State	Published - 2025.11.1

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Cite this

@article{301d38fd01d44822ad4d6275465de28a,

title = "State-of-charge estimation and prediction by machine learning models using experimental dataset of lithium-ion batteries based on ionic liquid modified LiFSI electrolyte",

author = "Akhtar, \{M. Shaheer\}",

year = "2025",

month = nov,

day = "1",

language = "Korean",

journal = "Materials Letters",

issn = "0167-577X",

}