# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 3 Proposed Method
### Proposed SOC and SOH Estimation Technique  
Figure 7 illustrates how the charge-discharge current and voltage profiles of a satellite battery vary over time and across seasons. A comparison between data from 1 December 2017 and 1 July 2017 reveals significant differences in the battery's charge-discharge patterns. In the December data, charge-discharge cycles occurred more frequently, with greater variations in the maximum and minimum current values. In addition, the voltage  
Figure 6: The relationship between the OCV and SOC. drops during discharge cycles were more pronounced. By contrast, the July data show less frequent charge-discharge cycles, smaller fluctuations in current, and a relatively stable battery voltage. These differences are likely attributable to factors, such as solar exposure duration, eclipse periods caused by Earth's shadows, and seasonal temperature variations, all of which influence a satellite's power generation and battery performance. This analysis highlights the impact of environmental conditions on the behavior of the satellite battery and the need for adaptive modeling to account for these seasonal and temporal variations. Therefore, accurately modeling the voltage-current characteristics of a satellite battery requires considering not only the simple current-voltage relationship but also a comprehensive range of factors. These include the satellite's mission state, date and seasonal variations, overall power consumption, and environmental temperature. The figure demonstrates how these variables influence the actual behavior of the battery. It is evident that simplified models are insufficient to fully explain the complex charge-discharge patterns observed in satellite operations. This shortcoming underscores the necessity of adopting data-driven modeling approaches that can effectively capture the intricate interactions between these variables and provide a more accurate representation of a battery's performance. Figure 8 shows the concept of deep learning-based voltage modeling and the process for predicting the states of satellite batteries in this paper. This process includes data collection and preprocessing, model training, battery voltage prediction, internal resistance estimation, and state of health (SOH) evaluation. First, the SOC of the battery is estimated using a UKF-based approach along with voltage and current telemetry data from LEO satellites. If the difference between the terminal voltage estimated via the state equation model and the measured voltage is below a defined threshold value, it is assumed that significant battery aging has not yet occurred. In this case, R\({}_{i}\) and SOH updates are not performed, and the SOC is continuously estimated online. On the other hand, if the difference between the estimated terminal voltage and the measured voltage exceeds the threshold, the discrepancy is attributed to battery aging. Under this assumption, R\({}_{i}\) is estimated offline using the UKF algorithm, and the SOH is updated accordingly. Figure 7: KOMPSAT-5 battery current and voltage profiles: (**a**) winter season and (**b**) summer season. In this process, the battery's voltage and current are utilized to detect aging by modeling changes in \(\mathrm{R_{i}}\) under the same SOC conditions during mission operations using a transformer network. To enable comparisons under identical SOC conditions, the user generates a virtual mission for a specific SOC condition, which is then input into the transformer network. The voltage profile generated by the network is used for analysis. When consistent SOCs and current conditions are maintained when detecting changes in \(\mathrm{R_{i}}\), the relative accuracy of the estimations improves. This approach ensures that the detected variations in \(\mathrm{R_{i}}\) are more precise and reliable, thereby enhancing the effectiveness of battery aging detection.