•An in-depth analysis of feature extraction from EIS is provided for SOH estimation.•Three features in SOH estimation are constructed using knowledge of battery EIS.•The Gaussian process regression is employed to realize the battery SOH estimation.•A comprehensive assessment of EIS features in SOH estimation is carried on.•The proposed SOH estimation is adaptive to different aging conditions. Battery state of health (SOH) estimation is a critical but challenging demand in advanced battery management technologies. As an essential parameter, battery impedance contains valuable electrochemical information reflecting battery SOH. This study investigates a systematic comparative study of three categories of features extracted from battery electrochemical impedance spectroscopy (EIS) in SOH estimation. The three representative features are broadband EIS feature, model parameter feature, and fixed-frequency impedance feature. Based on the deduced EIS features, a machine learning technique using Gaussian process regression is adopted to estimate battery SOH. The battery aging and electrochemical tests for commercial 18650-type batteries are performed, in which the constant and dynamic discharging conditions are considered during battery aging. The battery life-cycle capacity and EIS data are collected for the machine learning model. The performance of the constructed features is investigated and comprehensively compared in terms of estimation accuracy, certainty, and efficiency. Experimental results highlight that using the fixed-frequency impedance feature can realize outstanding performance in battery SOH estimation. The average of the maximum absolute errors for different cells under different aging conditions is within 2.2%.