## 1 Introduction  
With the improvement in residents' economic levels and the deepening of urbanization, there is increasing popularity of traditional fuel-powered vehicles [1], such as gasoline and diesel. The emission pollution, noise pollution, and electromagnetic pollution are becoming more and more serious, which has resulted in heightened energy consumption and environmental contamination [2]. Developing green transportation is an important way to address these issues, and new-energy electric vehicles have become an ideal choice for green transportation [3]. The biggest difference between new-energy electric vehicles and traditional gasoline vehicles is that their core power source is a battery [4]. This makes new-energy electric vehicles capable of zero emissions, high energy efficiency, low noise levels, and energy conservation. Among them, LIBs have characteristics such as high energy density, long cycle life, and low self-discharge rate [5], making them the primary power source for new-energy electric vehicles. However, during the use of LIB, rapid nonlinear aging issues may sometimes occur, severely limiting battery life [6]. When the LIBs of new energy electric vehicles encounter problems during operation, it is easy to cause traffic accidents and seriously affect traffic safety. Accurately predicting the SOH of LIBs is essential for providing decision support for timely maintenance and replacement of LIBs in new-energy electric vehicles. It serves as a crucial guarantee for the reliability and safety of the battery energy storage system, ensuring that the LIB can operate safely and effectively, and promoting road safety and environmental protection. Therefore, accurate prediction of LIB SOH is of paramount importance [7]. The SOH prediction is one of the important parameters for LIBs. It is usually defined as the ratio of the discharge capacity to the rated capacity of the battery under certain discharge conditions, also known as the capacity retention rate of the battery [8]. Early class methods for SOH prediction mainly include the definition method, electrochemical impedance spectroscopy method, and partial discharge method [9]. With the continuous development of electric vehicles, new prediction methods have been proposed. Currently, the widely used method is the model-based battery SOH prediction method, which estimates the SOH of LIBs by combining an electrochemical model or an equivalent circuit model with established state-space equations. Examples of these models include the second-order RC equivalent circuit model [10], fractional-order equivalent circuit model [11], and the core-buffer equivalent circuit model [12]. In recent years, with the widespread application of probability and statistical theory and the rise of big data, the machine learning model has also been applied in the field of LIB SOH prediction. Examples of these algorithms include the decision trees (DT) [13], the convolutional neural networks (CNN) [14], the long short-term memory neural networks (LSTM) [15], and the random forest (RF) [16], etc. These algorithms form a data-driven approach for the SOH of battery assessment. This approach does not require considering internal chemical changes in the battery. By analyzing the changes in current, voltage, capacity, and other data during battery charge-discharge processes, it can predict the SOH of the battery, providing a solid foundation for the research conducted for this study. The decision trees (DT) model is a common classification method and a straightforward machine learning algorithm [13]. It can classify and predict data, and it possesses advantages such as high accuracy, low computational cost, and high interpretability [17]. Based on the NASA LIB charge-discharge experimental data, [PERSON] et al. [18] proposed a model based on the gradient-boosting decision trees model framework to achieve evaluation of LIB SOH; [PERSON] et al. [19] predicted the SOH of an aging battery based on an enhanced decision trees model and analyzed its influencing factors; [PERSON] et al.