Abstract: With the widespread application of lithium-ion batteries in electric vehicles, energy storage stations, it is the key to ensure the reliability and safety of the battery to accurately estimate the health state of the battery. The complex electrochemical reaction inside lithium-ion battery and the changeable external use conditions make it challenging to realize accurate health state estimation and life prediction. With the rapid development of technologies such as artificial intelligence and big data analysis, the methods of battery health assessment have been gradually diversified. First, the aging mechanism and SOH concept of batteries are introduced in this article. Next, different SOH estimation methods are categorized into four classes：experiment-based, model-based, data-driven, and hybrid methods. The characteristics of each method are analyzed in detail, and the corresponding advantages and limitations in practical applications are compared. Finally, the future trends of SOH estimation are prospected.