Lithium-ion batteries (LIBs) are widely used in areas such as electrified transportation, electrochemical energy storage and mobile electronics. Consequently, accurate assessment of their state of health ( SOH) is fundamental to ensure safe and reliable applications. Data-driven methods are the mainstream methods to evaluate SOH, which do not need to consider the complex physical and chemical reactions inside the battery, and only rely on direct data analysis to achieve accurate SOH estimation. This paper analyzes the current research progress of data-driven estimation methods for battery SOH under the consideration of the influencing factors of SOH for LIBs, and focuses on comparing the principles, advantages and disadvantages of machine learning, filter and time series methods in implementing SOH estimation. Finally, according to the practical application scenarios of electric vehicles, the future development trend of SOH estimation methods is prospected.