## 1 Introduction  
Lithium-ion batteries (LIBs) are increasingly replacing lead-acid and nickel-based batteries due to their superior environmental and performance characteristics, including high power density, low self-discharge rate, long cycle life, and broad temperature range [1, 2, 3, 4]. With an increasing number of charge-discharge cycles, chemical degradation accelerates, contributing to the aging of the battery. This is caused by the continuous rise in internal resistance and the gradual reduction of the maximum usable capacity due to side reactions within the battery [5]. During real-world operation, harsh conditions and extreme environments can accelerate the aging process of LIBs, potentially leading to failure or thermal events. The SOH of LIBs is a critical parameter for battery management systems (BMS), as it provides an accurate assessment of the battery's aging status. In practice, replacing batteries with a low SOH, based on predefined thresholds, is a key strategy to ensure the safe operation of battery energy storage systems [6]. The United States Advanced Battery Consortium (USABC) recommends an SOH threshold of 80% for battery packs. When the SOH drops below this threshold, the battery is no longer suitable for use in electric vehicles [7]. Therefore, accurate and effective SOH estimation is crucial for the safe operation of electric vehicles and plays a key role in the widespread adoption of LIBs. SOH estimation methods for batteries are generally categorized into three main approaches: direct measurement, model-based, and data-driven methods. In the direct measurement approach, SOH is assessed by evaluating the battery's capacity and internal resistance [8]. Although this method is relatively simple to implement, it requires specialized equipment to assess the battery's internal resistance and capacity, imposing strict requirements on the testing environment. Therefore, it is only suitable for SOH estimation under controlled laboratory conditions and cannot be applied in practical settings [9]. Estimation methods based on battery models can be broadly classified into physical and mathematical models. The physical models include the equivalent circuit model (ECM) and the electrochemical model (EM) [10]. The internal chemical reactions in LIBs are complex, dynamic, and nonlinear, making them highly sensitive to external environmental factors and operating conditions. Due to the complexity of these reactions, identifying the parameters in the models is challenging. Additionally, anomalies during charging and discharging are difficult to explain, and the accuracy of these models' predictions often depends on extensive prior knowledge and experience [11]. Although SOH estimation methods based on battery models under specific operating conditions offer high accuracy, they require significant computational resources and are difficult to generalize, which complicates their implementation in practical BMS. Compared to the model-based approach, the data-driven approach does not require a detailed understanding of the internal mechanisms of the battery. Instead, it learns the variation in input data through iterative cycles, offering greater flexibility [12]. Data-driven methods have proven practical and are increasingly used for battery state estimation. In this approach, only the relevant health factors, such as voltage, current, and temperature, are extracted from the battery data. Machine learning or deep learning algorithms are then used to model the nonlinear relationship between these factors and the battery's SOH [13]. The accuracy of these methods depends on the BMS continuously monitoring and processing large amounts of real-time battery data [13]. Reference [14] highlights that, in data-driven approaches, the selection, processing, and modeling of prediction models play a key role in enhancing the accuracy of SOH estimation.