In electric vehicles (EVs), determining the State of Charge (SOC) is a challenging issue for the reliability and longevity of a battery management system (BMS). Lithium-ion batteries (LiBs) require precise state of charge (SOC) estimation in order to operate safely in electric vehicle applications and to prolong the cell’s lifespan. The conventional estimation of SOC approaches developed for the dynamic portraits have obstacles in the estimation of SOC for LiB because of their features of open circuit voltage central range of SOC. A Li-ion battery’s excessive complexity, time-variant nature, and non-linear properties make it difficult to estimate its state of charge accurately. Inaccurate SoC estimation increases possibility of hazardous events while driving. Through the use of Artificial Intelligence (AI) techniques, such as Deep Learning (DL) algorithms and Machine Learning (ML) models, several researchers have recently discovered effective methods for making precise estimates. The methods employed in this study for LiB prediction analysis, Battery modelling Methods analysis From this survey, conclude that the LiB estimation is a difficult and challenging task and various factors are involved for the estimation.