Thermal Performance Prediction of Satellite Battery Using Machine Learning: A Case Study

Satellite battery subsystems are fundamental for reliable operation during periods when solar power is unavailable. Their performance is strongly influenced by the harsh and variable thermal conditions of the space environment. Pre-launch thermal simulations are routinely employed to evaluate subsystem behaviour; however, their ability to fully represent in-orbit dynamics remains limited. Meanwhile, machine learning (ML) approaches have emerged for satellite health monitoring, yet their practical role alongside conventional thermal analysis is not clearly established. This study investigates satellite battery temperature prediction by integrating pre-launch thermal simulation, real in-orbit telemetry, and data-driven ML forecasting within a unified framework. A dataset of 27,552 temperature measurements from an operational low-Earth-orbit satellite was analysed. A sliding-window regression approach was used to predict one-hour-ahead minimum and maximum battery temperatures, consistent with operational thermal margins. Three models—linear regression, Random Forest, and Extreme Gradient Boosting—were trained using historical temperature data and evaluated against telemetry and simulation outputs using MAE, RMSE, and R2 metrics. Results indicate that linear regression achieved the highest accuracy (R2 up to 0.98, MAE ≈ 0.20 °C), outperforming more complex models. ML-based predictions captured thermal behaviour more effectively than static simulation outputs under nominal conditions, while all predicted values remained within the acceptable operational range (10–30 °C). Rather than replacing physics-based methods, this work demonstrates that interpretable ML models can serve as an effective real-time complement to thermal simulations, offering practical insights into model selection and enhancing satellite battery thermal monitoring.