## 2 Literature Survey Materials and Methods  
Li-ion batteries are characterized by several fundamental properties and performance parameters that make them highly effective for various applications. One of their fundamental features is high energy density, allowing them to store a significant amount of energy in a compact and lightweight form, which is crucial for portable electronics and electric vehicles. They also offer a long cycle life, meaning they can undergo numerous charge and discharge cycles with minimal capacity loss, contributing to their durability. Additionally, Li-ion batteries exhibit a low memory effect, which minimizes capacity reduction over time and usage. Their high charge efficiency ensures that most energy stored can be effectively used, reducing energy loss. The batteries also have a low self-discharge rate, meaning they retain their charge over time, which is beneficial for devices that are not frequently used. Key performance parameters include capacity, indicating energy storage,and voltage, representing nominal operating levels. Charging and discharging currents are also critical, determining how quickly the battery can be charged or provide power. Energy density, expressed in watt-hours per kilogram (Wh/kg) or watt-hours per liter (Wh/L), measures the amount of energy the battery can store per unit of weight or volume. Other important parameters include charging time, discharge time, cycle life, and operational temperature range, all of which influence the battery's usability and longevity. Safety features are also integral, protecting against overcharging, over-discharging, short-circuiting, and overheating. These characteristics and parameters collectively define Li-ion batteries' efficiency, reliability, and applicability across various technological fields [14; 15]. Depending on the modeling method used, Li-ion battery performance models are categorized into three primary types: electrochemical models [16; 17], mathematical models [18; 19], and electrical models [20; 21; 22]. Hybrid models that combine these approaches have also been proposed [23; 24]. Each type varies in accuracy and complexity. Electrical models are often preferred for integration into system-level simulations due to their balance of high accuracy and moderate computational complexity [25]. Key performance metrics such as battery life, charge-discharge cycles, and capacity degradation are critically defined and analyzed to assess battery systems' overall performance and reliability. Battery life is often discussed in terms of the number of charge-discharge cycles a battery can undergo before its capacity significantly diminishes. Capacity degradation is often quantified by comparing the battery's initial capacity to its remaining capacity after a certain number of cycles or over a specific period. These metrics are extensively reviewed and analyzed in academic and industry research to improve battery technologies, enhance performance prediction models, and ensure the reliability and efficiency of battery-operated systems across various applications. Furthermore, battery SOH defines a battery's current degradation level compared to its starting point of the lifecycle. SOH is mathematically calculated as the current battery capacity ratio to its initial value described in Equation (1). \[SOH=\frac{C_{current}}{C_{initial}}\times 100\% \tag{1}\]  
where current and \(C_{initial}\) denote a battery's current and initial capacity, respectively. Machine learning (ML) models are extensively utilized for data-driven state of health (SOH) estimation and remaining useful life (RUL) prediction due to their adaptability, which allows them to track the battery degradation process effectively [26; 27]. Achieving accurate SOH estimation necessitates a thorough understanding of the degradation processes that lead to battery failure. However, due to the complex electrochemical dynamics of batteries, grasping the degradation mechanisms is challenging [28], and the ability to match non-linear behaviors is limited [29].