## 1 Introduction
[20] used DTs to analyze the adaptability of battery to working condition, temperature and degradation, and found that the dynamic characteristics of battery changed significantly during aging. The convolutional neural networks (CNN) is a type of feedforward neural network that includes convolutional computations and has a deep structure. It is one of the representative algorithms in deep learning and possesses strong data feature extraction capabilities [14]. [PERSON] et al. [21] proposed integrating CNN and wavelet transform to make full use of the ability of CNN to extract features hierarchically, and wavelet transform to analyze frequencies, so as to predict the SOH of the battery; [PERSON] et al. [22] used CNN to extract features from the collected image data of a large number of common battery manufacturing faults, reducing human intervention and saving time; [PERSON] et al. [23] proposed a CNN-based model to explain the relationship between the charging data and the SOH, so as to diagnose the SOH of the LIB; [PERSON] et al. [24] proposed using 1D-CNN technology to learn the nonlinear relationship between VRB current, flow rate, state-of-charge and voltage. The long short-term memory neural network (LSTM) is a type of recurrent neural network designed specifically to address the issues of class recurrent neural networks. LSTM is well-suited for processing and predicting important events with long gaps and delays in time series data [15]. [PERSON] et al. [25] proposed a method for estimating SOH of LIB using the LSTM network, and the estimation accuracy is higher than that of the feedforward neural network; [PERSON] et al. [26] compared the prediction accuracy of two algorithms, EKF-LSTM and LSTM-EKF, for the SOH of the battery. The results showed that the LSTM-EKF method achieved the highest estimation accuracy; [PERSON] et al. [27] established an improved LSTM model to predict the SOH of the LIB, and the results show that the prediction accuracy was higher than the LSTM model without optimization;[PERSON] et al. [28] conducted experiments using NASA and CALCE battery charge-discharge datasets. The results demonstrated that the prediction method based on the LSTM model effectively forecasts the available capacity and remaining useful life of the LIB. Random forest (RF) is an ensemble learning algorithm initially introduced by [PERSON] in 2001. It possesses strong data mining capabilities, high accuracy, robustness, and interpretability [29]. The fundamental principle of RF involves randomly sampling the data from the datasets to construct new subsets, which are then used to train multiple decision trees. These decision trees are combined to create a more powerful model, thereby enhancing predictive accuracy [30]. [PERSON] et al. [31] suggested a preference for using the RF model in machine learning due to its ease of use and minimum number of hyperparameters for tuning. Compared to machine learning models such as DT, CNN, and LSTM, the RF model has demonstrated higher predictive accuracy in certain application scenarios [32; 33; 34]. The aforementioned studies indicate that the RF model may offer better predictive performance in data prediction. To this end, there have been some studies that combine the RF model for battery SOH prediction. For example, [PERSON] et al. [35] proposed a battery SOH estimation method based on constant current charging time, combining it with RF regression to achieve accurate and fast SOH prediction. [PERSON] et al. [36] introduced a multi-feature-based multi-model fusion approach for LIB SOH estimation, enhancing the prediction accuracy by incorporating an RF model for preliminary SOH estimation. These studies have established an important model foundation for conducting research on the SOH prediction of LIB in new-energy electric vehicles in this paper.