# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 5 Conclusions  
In this study, we present a novel data-driven method for estimating the degradation of lithium-ion batteries in LEO satellites. By leveraging a transformer network for voltage prediction and a UKF algorithm for online state estimation, the proposed approach enables the accurate prediction of the SOH under varying operating conditions. Through the integration of machine learning-based modeling and real telemetry data from the KOMPSAT-5 satellite, this method successfully addresses the limitations of traditional degradation estimation approaches, which often depend on predefined conditions and extensive user expertise.  
The results demonstrate the ability of the proposed method to accurately capture nonlinear battery behavior and provide reliable degradation predictions over time, minimizing the need for manual data processing and allowing consistent monitoring of battery health, even in the resource constrained environments typical of LEO satellite operations.  
This research study contributes to the advancement of battery health diagnostics and the operational sustainability of LEO satellites. These findings indicate that the proposed technique can serve as a robust and practical tool for satellite power system management, ensuring reliable mission performance. Future work will focus on enhancing the scalability of this method for multi-satellite constellations and further improving its ability to meet the demands of evolving satellite missions.  
In this study, we developed a method for learning from in-orbit data and estimating battery degradation on the ground to reduce the processing load on a satellite's limited onboard system. The satellite used in this study was designed and operated based on a ground-based mission approach, which did not require real-time battery degradation estimation. Consequently, network and filter optimization for lightweight implementation were not performed, limiting the throughput of the onboard satellite.  
To address this, future research will focus on optimizing onboard networks and filters for real-time degradation estimation in satellites wherein such capabilities are required. Additionally, we confirmed that the proposed voltage profile modeling method maintained consistent error levels in both the summer and winter, despite variations in KOMPSAT-5's operational conditions and environment, suggesting that this method may be applicable to other satellites subject to different operational conditions.  
Future research will further explore this possibility by extending the approach to geostationary satellites with different mission and environmental conditions as well as multi-satellite clusters, ensuring scalability and adaptability.  
Conceptualization, S.-T.Y.; methodology, K.-S.P. and S.-T.Y.; validation, K.-S.P.; formal analysis, K.-S.P. and S.-T.Y.; writing--original draft preparation, K.-S.P.; writing--review and editing, S.-T.Y.; visualization, K.-S.P.; supervision, S.-T.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a research grant from Gyeongsang National University in 2023.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.