## 1 Introduction  
As the number of vehicles powered by fossil-fuel-burning internal combustion engines has increased, carbon dioxide emissions have also increased, and the climate change problem has emerged as a global topic. To overcome this problem, studies on electric vehicles (EVs) to replace internal combustion engine vehicles have been gaining momentum [1]. Lithium-ion batteries (LIBs) are used as an energy source in EVs because of their advantages, which include a high energy density, long life, and high efficiency [2]. A LIB may overheat as a result of over-charging and over-discharging depending on various driving conditions for the EV, causing fires and explosions [3, 4]. In other words, the state of a LIB has a significant effect on the safety of an EV, which implies that real-time estimation of the battery state is imperative [5]. Real-time estimation of the battery state refers to estimations of its state of health (SOH) and state of charge. Among these, its SOH is an important indicator of battery life and is defined as a percentage of the capacity of the fully charged battery at the current point in time relative to the initial capacity [6]. As a battery's deterioration progresses, its lifespan decreases, reducing its safety [7]. Therefore, its SOH is an indicator that is directly related to the safety of the battery, and it is crucial to estimate it accurately [8, 9, 10]. The performance of a LIB deteriorates as a result of a variety of conditions, such as repeated charging and discharging, its internal temperature, and charging and discharging current [11, 12]. Therefore, it is difficult to clearly formulate a method for determining the performance degradation of a battery. It is necessary to develop an algorithm that can accurately estimate the SOH [13]. Methods for estimating the SOH of a battery include model- and data-based estimation methods. Model-based estimation methods use an electrochemical battery equivalent circuit model and adaptive filters to estimate the SOH [14]. The adaptive filters used include the Kalman filter (KF) and particle filter (PF). The KF uses a recursive method to compare the previous estimate and the current measured value to reduce errors, thereby increasing the estimation accuracy [15]. However, a regular KF is used in linear models and is not suitable for battery models that include nonlinearity. Therefore, [PERSON] et al. [16] estimated the SOH by linearizing a nonlinear system with an extended Kalman filter (EKF). However, the EKF produces an estimation error because the state variable is estimated by linearizing the nonlinear system through a Jacobean matrix [14]. [PERSON] et al. [17] used an unscented Kalman filter (UKF) to estimate the SOH. The UKF facilitates more accurate SOH estimation because the state variable is estimated relatively error-free by using approximates without obtaining the Jacobian matrix for linearization, unlike the EKF. In the case of the aforementioned EKF and UKF, the noise must satisfy a Gaussian distribution. In contrast, the PF method is suitable for estimating the state of a LIB, which is a nonlinear and non-Gaussian system because of various electrochemical reactions [18]. The PF finds random particle samples with weights that are related to the post-probability density. When this process is repeated, the weight tends to become 0 [19]. To solve this problem, [PERSON] et al. [20] estimated the SOH by providing a distribution closer to the actual post-distribution through an unscented particle filter (UPF) that combined the UKF and PF algorithms. However, there were limitations in solving physical and chemical changes and various noises in the LIB model [21]. Meanwhile, data-based methods estimate the SOH by using a machine learning approach without requiring the analysis of electrochemical reactions [22]. Recently, many studies have been conducted using data-based methods. [PERSON] et al. [23] analyzed data to find patterns and estimated the SOH using the support vector machine, which is an algorithm for approximating nonlinearities.