In response to the stalling fault of the solar array drive assembly (SADA) in an in-orbit MEO satellite, an analysis and research on the energy balance algorithm are conducted. This is performed under the continuous changes in the light period, shadow period, and the incident angle of the solar panels. An output energy model of the solar panels is presented. It is proven that this model is a continuous function, and the optimal stalling angle for energy output is deduced. By simulating and calculating the energy output under different stalling angles and taking into account the on-orbit performance degradation of the solar cell array, the energy output curve within one orbital period is obtained, which provides support for the on-orbit operation and maintenance of the satellite. Moreover, on-orbit verification was carried out in the case of a stalling fault of the -Y-wing SADA of a certain MEO-orbiting satellite.

This paper focuses on the triple-junction gallium arsenide solar array of a MEO (Medium Earth Orbit) satellite that has been in orbit for 7 years. Through a combination of theoretical and data-driven methods, it conducts research on anti-radiation design verification and life prediction. This study integrates the Long Short-Term Memory (LSTM) algorithm into the full life cycle management of MEO satellite solar arrays, providing a solution that combines theory and engineering for the design of high-reliability energy systems. Based on semiconductor physics theory, this paper establishes an output current calculation model. By combining radiation attenuation factors obtained from ground experiments, it derives the theoretical current values for the initial orbit insertion and the end of life. Aiming at the limitations of traditional physical models in addressing solar performance degradation under complex radiation environments, this paper introduces an LSTM algorithm to deeply mine the high-density current telemetry data (approximately 30 min per point) accumulated over 7 years in orbit. By comparing the prediction accuracy of LSTM with traditional models such as Recurrent Neural Network (RNN) and Feedforward Neural Network (FNN), the significant advantage of LSTM in capturing the long-term attenuation trend of solar arrays is verified. This study integrates deep learning technology into the full life cycle management of solar arrays, constructs a closed-loop verification system of “theoretical modeling–data-driven intelligent prediction”, and provides a solution for the long-life and high-reliability operation of the energy system of MEO orbit satellites.

Based on the analysis of solar array current data from a certain MEO-orbiting satellite, this paper reveals its short-period fluctuation characteristics and underlying mechanisms. The study finds that when solar panels face the sun during the light period, the output current exhibits significant short-period fluctuations in addition to being influenced by long-period factors such as sun–earth distance, incident light intensity changes, and space irradiation attenuation. Through theoretical analysis, we first confirm that the root cause of these short-period variations is the temperature change in the shunt circuit caused by load fluctuations, which in turn affects the output current characteristics. Unlike traditional methods that use static characteristic factors such as incident angles, this paper innovatively proposes using load current as a key characteristic factor. For asymmetric solar panel fault scenarios, load current, time phase, and fault-wing output current are used as characteristic factors to adaptively predict the current of normal wings. Meanwhile, feedforward neural network (FNN), Recurrent Neural Network (RNN), and long short-term memory (LSTM) are used for output current prediction. The experimental results show that these methods can accurately capture the short-period fluctuations caused by load mutations and adapt to the fluctuation trend of the normal wing during the prediction of current changes in the faulty wing. It is worth noting that, limited by the short-period fluctuation prediction scenario, the inherent advantage of LSTM in long-sequence prediction is not fully reflected.

Faced with the complex space electromagnetic environment, the long solar illumination periods of medium-Earth-orbit (MEO) satellites, and the unique operating limitation under which the batteries operate under shallow charge–discharge cycles, this paper proposes a dedicated life evaluation method suitable for MEO satellite batteries. The proposed method quantifies battery performance degradation by analyzing the statistical correlation between the discharge capacity and discharge cut-off voltage during eclipse phases. Using seven years of on-orbit-measured data covering 14 eclipse seasons, we build prediction models for the cut-off voltage via Random Forest and XGBoost, with the eclipse season index and discharge capacity set as model input features. The mean squared error (MSE) of both models is lower than 0.003, demonstrating the high reliability of the modeling framework. Under the condition of a fixed discharge capacity, no obvious battery degradation exceeding the measurement uncertainty is detected within the observation period. The magnitude of potential voltage degradation is comparable to or even smaller than the 2 mV acquisition uncertainty, such that no measurable degradation phenomenon can be identified in the available dataset. At present, long-term on-orbit-measured data of MEO satellite batteries are scarce in engineering research. This study provides valuable full-measurement data support for the optimized design, autonomous on-orbit management, and life evaluation of NCA lithium-ion batteries and fills the gap in long-term on-orbit datasets for MEO satellite power systems.