## 1 Introduction
Furthermore, reference [15] emphasizes that high-quality health features (HFs) such as voltage HFs, current HFs, internal resistance HFs, and energy HFs during the charging process are crucial for accurately reflecting the degradation trends of the battery's SOH. Machine learning techniques, such as (support vector machines, SVM), (gated recurrent units, GRU), and (long short-term memory, LSTM), have been increasingly applied in SOH prediction [16]. For example, Reference [17] shows SVM to estimate the battery's SOH, by utilizing the constant voltage charging and discharging times of LIBs as input variables and SOH as the output; an accurate SOH estimation was achieved through the training of an SVM model. Reference [18] shows a transfer learning-based (Gaussian process regression, GPR) model was proposed to achieve unified SOH prediction without causing prediction delays. Reference [19] shows a method based on the (time pattern attention, TPA) mechanism and (convolutional neural network--long short-term memory network, CNN-LSTM) model to improve the accuracy of SOH estimation. In model design, when the structure and parameters of data-driven models are not flexibly adjusted to the battery's characteristic data, it often results in poor generalization performance. However, the design of hyperparameters such as learning rate, number of layers, and hidden units, along with the complex architecture of LSTM and GRU models, makes training more challenging. This complexity may lead to overfitting, particularly with small sample sizes, thereby affecting the stability and accuracy of SOH prediction [20]. On the other hand, SVM, which optimizes by maximizing the margin between classes, exhibits strong nonlinear fitting capabilities, but it has limitations in handling large-scale data, hyperparameter tuning, noise tolerance, and computational efficiency. To address this issue, the least squares support vector machine (LSSVM) model is introduced to establish a prediction model that captures the nonlinear relationship between SOH and feature data [21,22]. The performance of LSSVM models is strongly influenced by the optimal selection of hyperparameters, particularly the regularization factor and the kernel function type. Heuristic optimization techniques, such as particle swarm optimization (PSO), grey wolf optimization (GWO), and so on, are commonly used to optimize these parameters and enhance model accuracy [23]. Although these methods can improve prediction accuracy to some extent, models based solely on a single algorithm often face challenges such as slow convergence and a tendency to become trapped in local minima, limiting their applicability in real-world scenarios [24]. Additionally, outliers can negatively affect the stability and robustness of the LSSVM model, significantly reducing its performance when handling noisy data. To enhance the accuracy of SOH prediction and achieve precise estimation of the SOH of LIBs, this paper introduces a novel approach that combines the PSO-GWO-LSSVM model with the extraction of multi-dimensional HFs. The main innovations of this method include: (1) to effectively capture the LIBs aging process, eight HFs were extracted from battery charging segments and categorized into four main types: time-based, energy-based, similarity-based, and second-order HFs; (2) correlation analysis of the extracted HFs identified six highly correlated parameters; (3) to optimally determine the regularization and other parameters of the LSSVM, the PSO-GWO optimization algorithm is introduced. This method leverages the strengths of both algorithms, balancing global and local search capabilities to address issues such as overfitting, underfitting, and convergence to local optima; (4) The PSO-GWO-LSSVM model is subsequently developed for SOH prediction, providing high accuracy.