In order to comprehensively show the research progress of the estimation method of the residual power of Lithium-ion batteries, this paper reviewed the relevant papers and patents in the databases of Web of science, cnki, the patent library of the China National Intellectual Property Administration et al since 2013, and summarized the mainstream estimation methods of the residual power of Lithium-ion batteries. This article summarizes the estimation errors of commonly used direct estimation methods (ampere hour integration method, open circuit voltage method, and impedance characterization), methods based on equivalent circuit models, methods based on electrochemical models, and methods based on artificial intelligence neural networks for estimating the remaining battery capacity of Lithium-ion batteries. The results show that the maximum estimation error of ampere hour integration method can reach 15%; the maximum estimation error of the open circuit voltage method is 12.4%; the average estimation error of electrochemical impedance spectroscopy is less than 3.8%; the estimation error of kalman filtering method is less than 1%; the average error of particle swarm filtering method can be less than 1%; the average error of the method based on electrochemical model is less than 2%; the average error of neural network-based methods is less than 2%; the maximum error of the multi method mixing and multi parameter joint estimation method is less than 5%, and the average error is less than 2.5%. The results indicate that the kalman filter method has higher accuracy and is easier to implement compared to direct estimation methods and other model-based methods; the method based on neural networks can obtain more accurate results without analyzing the battery model; the mixed use of multiple methods and the use of multiple parameters to correct the estimated values have further improved the estimation accuracy. This article also compares and analyzes the estimation accuracy, advantages, difficulties, and applicable battery types of various methods for estimating remaining power in electric vehicles and implantable medical electronic devices. It clarifies the specific application plans of estimation methods and looks forward to the development direction of estimation methods in these two fields. This article can provide comprehensive and detailed information on the research status and development direction of Lithium-ion battery remaining capacity estimation methods for researchers and practitioners in related fields.