## 2 Related Works  
Reducing greenhouse gas emissions has grown to be a major worldwide problem driving the fast acceptance of renewable energy sources and electric cars (EVs). Thanks to their increased energy density, longer cycle life, and efficiency, lithium-ion batteries (LIBs) have become the pillar of energy storage options. These characteristics make LIB's essential for running electric vehicles and other electrical devices. RUL refers to the period, often expressed as the number of charge-discharge cycles, during which a lithium-ion battery remains functional before its capacity declines to 80% of its original value, as defined by industry standards such as those from the U.S. Department of Energy and IEC 61960 [1,2]. This threshold marks the battery's end-of-life (EOL) and is widely used to assess performance degradation. Due to its critical role in ensuring reliability, safety, and operational efficiency, RUL prediction has received significant attention in battery research. Complicated electrochemical events influencing performance over time define battery deterioration. Primary markers of battery health have been shown by studies to include capacity fade, rising internal resistance, and voltage profile alterations [3]. Accurate prediction of RUL and implementation of efficient battery management systems (BMS) depend on an awareness of the degradation processes. Several studies have explored the RUL of these batteries. Moreover, it is crucial to emphasize that effective management and forecasting of battery utilization can greatly enhance its efficiency. This, in turn, fosters the adoption of clean energy, diminishes emissions,and stimulates innovation and collaboration. Understanding the RUL is crucial for effective planning and operation in battery-dependent systems. [PERSON], [PERSON], et al. suggested that optimizing battery usage and prolonging their lifespan can improve the sustainability of energy-powered systems and encourage the integration of renewable energy. The duration can be quantified in various units such as days, years, miles, cycles, or any other numerical representation, contingent upon the system in use [4]. RUL prediction provides early warnings of potential failures and has developed into an essential component of system prognostics and health management. RUL prediction helps proactive maintenance spot possible machinery or equipment breakdowns before they start. RUL prediction helps to estimate the time left until a system or component reaches the end of its useful life, facilitating prompt maintenance interventions, lowering downtime, minimizing repair costs, and making the best use of assets. [PERSON], [PERSON], et al. underlined that RUL prediction is important in sectors where unscheduled downtime may cause major financial losses, safety risks, and operational disturbance [5]. Good RUL prediction calls for a thorough awareness of the systems operational traits, failure modes, and degradation mechanisms. One can generally divide RUL prediction methods into data-driven and model-based systems. Both approaches have special benefits and drawbacks; recent developments have concentrated on hybrid strategies combining the characteristics of both [6]. Since it guarantees timely maintenance of electric vehicles and effective reuse of second-life batteries, predicting the RUL of a lithium-ion battery with limited deterioration history is particularly significant [7]. Precise state-of-health (SOH) prediction is crucial for ensuring operational safety and preventing hidden failures in lithium-ion batteries. The advancement of communication and artificial intelligence technologies has led to a significant amount of investigation into accurate and dependable state-of-health prediction methods utilizing machine learning techniques [8]. Ensuring the safety of lithium-ion batteries over their entire lifespan is essential. [PERSON] et al. [9] proposed a machine learning-based framework for early prediction of lithium-ion battery lifetime using real-world cycling data.