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State of health (SOH) is essential for battery management, timely maintenance, and safety incident avoidance. For specific applications, a variety of SOH estimation methods have been proposed. However, it is often difficult to apply these methods to other applications. In this article, a novel feature extraction method is proposed to extract health indicators (HIs) from general discharging conditions. A voltage partition strategy is used to obtain the discharge capacity differences of two cycles [△Q(V)] from nonmonotonic or pulse discharge voltage curve, and a filtering strategy is employed to obtain smooth voltage curves under dynamic discharging conditions. The standard deviations of the discharge capacity curve and △Q(V) are selected as HIs and are verified to have strong correlations to battery capacity under different datasets for three types of batteries. By using these HIs as input features, typical data-driven …