# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 4 Experiments and Results
In addition to the SOC, variables such as the magnitude of the discharge current, duration, and temperature conditions must be analyzed in combination to extract information directly related to battery aging. As the quantity of data increases, the complexity of these tasks also increases, significantly extending the time required for data analysis. Owing to these challenges, it is difficult to perform DCIR-based aging modeling over short periods. Battery degradation typically progresses slowly, requiring long-term data collection and analysis in order to build a reliable model. If performed by inexperienced users, this process can lead to data errors or reduced reliability of the results of the analysis. However, when the conditions are properly filtered and the analysis is systematically conducted, DCIR can provide reliable information on battery aging. In this study, the SOH values derived from the DCIR-based analysis conducted by ground users were compared with the results of the proposed aging estimation technique. This comparison highlighted the practical application of the proposed method and its greater effectiveness in assessing battery aging relative to established techniques. Figure 12 compares the results of the three methods for estimating battery aging. The first method involves ground-based predictions (the blue line) conducted during the pre-launch design phase using the expected current cycles. This approach provides a conservative estimate of battery aging, predicting relatively rapid performance degradation over time. The second method represents on-orbit aging estimation (the green line), in which experienced users selectively analyze the data to predict battery aging. This method reflects realistic data trends, resulting in a less steep degradation curve compared to those obtained with design phase predictions. The third method is the proposed data-driven approach (the red line). The results show that the proposed estimation (the red line) aligns closely with the values obtained from the satellite expert at the ground station (the green line). In the case of the green line, data selection for aging analysis requires users to define criteria based on their experience and long-term trend analysis, making the process time consuming (lasting over one day). Additionally, the associated costs in terms of analysis time and effort limit the feasibility of frequent evaluations. By contrast, the proposed method is leveraged to deliver accurate and reliable battery aging information within an hour. This approach also enables repetitive analyses at shorter intervals. Notably, the proposed method involves the use of on-orbit data to produce results that closely reflect actual battery conditions. It effectively captures complex aging trends that emerge over time, making it a powerful tool for monitoring and estimating battery aging in orbit. In conclusion, the proposed aging estimation approach enhances the reliability and accuracy of SOC estimations by effectively reflecting performance degradation during the operational lifespan of a battery. Figure 13 illustrates the results of SOC estimation obtained using the proposed aging estimation approach, the results of SOC estimation obtained using the aging estimation with various mission condition data with the UKF, and SOC estimation with aging estimation analyzed by a satellite expert at the ground station. Herein, the comparison is between the results for (a) the early phase of satellite operation and (b) 51 months after launch. Figure 12: Battery SOH estimation results over the operational period. Figure 13: Battery state of charge (SOC) estimation results for the KOMPSAT-5 satellite: (**a**) 3 months after launch; (**b**) 51 months after launch. On orbit, the reference SOC of the satellite battery was determined by utilizing all available orbital data, which were preprocessed and selectively filtered by satellite experts.