## 1 Introduction
The RF model has five major parameters: maximum depth of decision trees (max_depth), number of decision trees (n_estimators), feature selection (max_features), minimum samples required to split an internal node (min_samples_split), and minimum samples required for a leaf node (min_samples_leaf). Most of these five parameters are often set according to human experience in practical applications, which can potentially impact the prediction accuracy. For instance, in the literature [35; 36], there has not been adaptive tuning of the RF model parameters. In this regard, some experts and scholars have proposed improvement methods for the RF model regarding the handling of model parameters. For example, [PERSON] et al. [37] improved the accuracy of point cloud classification by using weighted voting and modifying n_estimators. [PERSON] et al. [38] introduced the RandomizedSearchCV and GridSearchCV methods to optimize n_estimators and max_depth, thereby enhancing the average accuracy. [PERSON] et al. [39] considered configuring a large number of parameters during the improvement of RF but did not perform parameter optimization. [PERSON] et al. [40] used particle swarm optimization (PSO) to optimize the parameters of max_depth, n_estimators, and max_features, enhancing the control accuracy of slurry pressure. [PERSON] et al. [41] utilized PSO to optimize the parameters of n_estimators, max_features, and min_samples_leaf, achieving high classification accuracy for ultra-high-voltage converter valve fault detection. The aforementioned research has provided important insights for further developing an RF improved model in predicting the SOH of LIB in new-energy electric vehicles. However, these studies, on one hand, only optimized some parameters among the five major parameters of the RF model, without delving into the impact of other unoptimized parameters on the predictive accuracy of the model. The optimization of combinations of these five parameters may further enhance the predictive accuracy of the RF model, but could simultaneously increase the model's complexity and runtime. Therefore, there is a need to select a search algorithm to enhance the optimization performance of model parameters. The PSO algorithm is a global optimization search known for its high convergence and fast search speed [42], making it suitable for addressing the parameter optimization problem of the RF model [40; 41]. It can adaptively optimize the five major parameters of the RF model, namely max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf, thus improving both the model's training speed and predictive accuracy. Currently, there is limited research on using PSO to enhance the predictive accuracy of LIB SOH through optimizing the RF model. In this regard, we focus on two main areas of innovation in the context of SOH prediction for LIBs in new-energy electric vehicles. First, it involves the analysis of SOH prediction using various machine learning models based on multiple 18,650 LiFePO4 batteries charge-discharge cycle experimental data. This analysis validates the feasibility of applying the RF model to SOH prediction for LIBs in new-energy electric vehicles. Second, the study employs a PSO algorithm to adaptively tune the five major parameters of the RF model. By utilizing the improved RF model, it further improves the accuracy of LIB SOH prediction, contributing to the development of new-energy electric vehicles and green transportation.