Among the existing methods for battery sorting, ML methods stand out with superior merits, which avoid conspiring the complex internal electrochemical reactions inside batteries thus establishing the relationship between features and battery capacity directly. In addition, ML could achieve fast and reliable sorting on the basis of fast testing data or historical data. At present, the application of ML methods in battery sorting has made significant strides, gradually surpassing classical sorting methods reliant on experimentation. In such approaches, voltage curves serve as the primary source of input features , with LIBs being the most commonly studied technology. Moreover, neural networks emerge as the dominant method , varying from simple three-layer models (most prevalent) to sophisticated deep learning architectures.