# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 1 Introduction  
The rapid advancement of satellite technology has contributed significantly to various fields, such as communications, navigation, Earth observation, and scientific research. Among these, low-Earth-orbit (LEO) satellites play a vital role owing to their proximity to Earth, offering high-resolution imaging, low signal latency, and relatively low launch costs. However, the operation and lifespan of LEO satellites depend heavily on their power systems, particularly the batteries that supply them with energy in conditions wherein solar power is insufficient. Battery aging has a direct impact on mission performance, reliability, and operational lifespan [1]. Over time, batteries undergo complex chemical and physical changes, resulting in capacity reduction and increased internal resistance. These changes not only lead to a decrease in energy storage capacity but, if not addressed in a timely manner, may also result in mission failure [2]. Therefore, accurately and efficiently diagnosing battery aging is essential to ensuring the optimal performance and sustainability of satellite missions [3, 4, 5, 6]. The accurate diagnosis of battery health in satellite systems is critically important from both technical and economic perspectives [7, 8]. First, unlike ground-based equipment, satellite batteries cannot be physically replaced, and without proper predictions and corresponding mission design adjustments, abnormal operations may occur, leading to mission losses during specific periods [9]. Second, as the global satellite market continues to expand, cost-efficient satellite operations are becoming increasingly vital [10]. The accuratediagnosis of battery aging enables the maximization of satellite mission performance and operational efficiency [11,12]. Conventional methods for diagnosing satellite battery aging in orbit rely primarily on trend analysis through periodic monitoring and user-experience-based assessments. However, these approaches are time consuming and can have limitations in applying consistent criteria and achieving accurate analyses owing to variabilities in user judgment and satellite operation conditions. In contrast, advancements in big data and machine learning technologies, along with the application of Kalman filters, have driven research aimed at improving the accuracy and efficiency of battery health diagnostics, as seen in ground-based systems, such as automotive applications. However, space systems, particularly LEO satellites, face unique challenges. Their communication windows are generally limited, restricting the availability of real-time data. In addition, satellite systems often utilize space-hardened processors, which impose constraints on computational capacity, further complicating the implementation of data-driven diagnostic approaches in these environments. Therefore, in this study, we propose a novel technique for estimating the charge and discharge states of LEO satellite batteries online based on using the unscented Kalman filter (UKF) algorithm and for diagnosing battery aging offline based on using a data-driven transformer network-based voltage prediction model. By integrating advanced machine learning techniques with domain knowledge specific to satellite systems, the proposed algorithm is designed to enhance the accuracy of battery aging predictions while minimizing the need for extensive manual intervention by users. Furthermore, by leveraging historical and real-time satellite telemetry data, this algorithm identifies patterns and anomalies indicative of battery aging. This capability provides valuable insights into predictive maintenance and satellite health management, thereby contributing to the efficient and reliable operation of satellite power systems. The main contributions of this research study are as follows:  
* A data-driven learning- and UKF filtering-based technology for modeling the degradation states of lithium-ion batteries in LEO satellites was developed.