## 1 Introduction  
The current international community is attaching ever-increasing importance to environmental protection, and the development principle of \"low-carbon and environmentally friendly\" is being advocated worldwide. In this context, renewable energy, distributed energy storage, and low-carbon energy sources are attracting significant interest. For instance, in the case of common lithium cobalt oxide batteries, the cathode material is lithium cobalt oxide, and the anode material is graphite. Lithium batteries achieve energy storage and release through the intercalation and de-intercalation process of lithium ions between the cathode and anode [1; 2]. The chemical equation for this process is as follows:  
\[\text{LiCoO}_{2}+\text{C}_{n}\rightleftharpoons\text{Li}_{1-X}\text{CoO}_{2}+ \text{Li}_{X}\text{C}_{n}\]  
Due to it's energy-saving and environmentally friendly nature, ease of portability and use, and high energy density and portability, lithium-ion batteries find widespread applications in various fields, such as portable electronic communications, transportation, and energy storage systems, making them favored across different industries [3; 4]. However, lithium batteries also pose certain potential risks during usage. Improper charging and discharging behaviors can lead to overcharging or over-discharging, causing an increase in the temperature of lithium batteries and accelerating their aging process [5; 6]. This can result in safety issues such as internal short circuits, oxidation, corrosion, and thermal runaway. Therefore, ensuring the safe operation through the estimation of lithium-ion battery health status holds significant societal implications [7], as it contributes to driving battery technology advancements, resource conservation, and fostering sustainable development. Currently, various methods are used to estimate the State of Health (SOH) of lithium-ion batteries, mainly including model-based approaches, data-driven methods, and fusion methods [8; 9; 10]. Model-based methods involve building various battery models to acquire relevant model parameters related to SOH. For instance, [PERSON] et al. [11] proposed an electrochemical model that describes the distribution of lithium-ion content within the battery. They used recursive least squares for online parameter identification and employed an Unscented Kalman filter for battery state estimation. Methods based on electrochemical models consider the complex internal physical structure of batteries and the chemical reactions occurring at the positive and negative electrodes, thereby providing a better understanding of battery performance degradation patterns. These methods are applicable to batteries with different material systems. However, these methods require a deep understanding of the battery's electrochemical characteristics and dynamic behavior, as well as a comprehensive collection of experimental data and precise calibration calculations. On the other hand, data-driven methods establish a non-linear mapping between Health Factors (HF) and the State of Health of lithium-ion batteries by analyzing and processing a large volume of experimental or observational data to address the problem. Common machine learning algorithms used are Extreme Learning Machine (ELM) [12], Long Short-Term Memory (LSTM) [13], and various other algorithms. Data-driven methods estimate through the mapping relationship between data input and output, making them suitable for various types and specifications of lithium-ion batteries, as well as State of Health predictions in different application scenarios. They can perform fast analysis and prediction based on specific problems and data, exhibiting high flexibility. Moreover, with ample data, they show superior performance. However, in data-driven methods, the predictive model may be perceived as a \"black box\", with lower transparency in its internal workings and decision-making processes.