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Electronics
Volume 14
Issue 23
10.3390/electronics14234697
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction

by
Shuo Jiang
1,2,
Boyu Wang
3,
Xuan Zhang
2,
Yaoxian Jiang
2,
Shuyi Liu
4,
Zhenyu Zhao
5,
Ruide Li
3,6,* and
Xiao Chen
3
1
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2
Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing 100863, China
3
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
4
China Satellite Network Application Co., Ltd., China Satellite Network Group Co., Ltd., Beijing 100190, China
5
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
6
Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, Zhejiang 314019, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(23), 4697; https://doi.org/10.3390/electronics14234697 (registering DOI)
Submission received: 1 November 2025 / Revised: 25 November 2025 / Accepted: 25 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Emerging Trends in Space–Air–Ground Integrated Networking and Communication Systems)
Versions Notes

Abstract

ingSatellite battery status prediction is crucial for ensuring the healthy operation of future satellite constellations. However, traditional telemetry-based methods, where satellite battery status is transmitted in real time to ground stations for processing, consume significant satellite bandwidth and introduce response delays. Advances in onboard computing and federated learning (FL) enable local model training and centralized parameter aggregation, reducing transmission overhead while leveraging distributed satellite data. Nevertheless, the unique orbital motion of satellites presents challenges for FL, primarily due to battery status heterogeneity arising from varying sunlight exposure. Limited onboard energy further necessitates balancing model performance with battery efficiency during local training. To tackle these issues, we propose ObsBattery—a position-aware FL framework that clusters satellites based on their orbital positions to improve model accuracy. ObsBattery employs a Dueling Deep Q-Network to dynamically determine satellite clustering and adapt local training rounds according to power availability, thereby reducing energy consumption during low-power phases. Evaluations on a real-world satellite battery dataset show that ObsBattery significantly improves both prediction accuracy and energy efficiency. Compared to a standard clustered FL approach, it reduces model MAE by 16% and energy consumption ratio by 6% under experimental conditions.
Keywords: satellite battery status prediction; federated learning; dueling deep Q-network; orbital position clustering; energy efficiency; satellite constellations; distributed machine learning satellite battery status prediction; federated learning; dueling deep Q-network; orbital position clustering; energy efficiency; satellite constellations; distributed machine learning

Share and Cite

MDPI and ACS Style

Jiang, S.; Wang, B.; Zhang, X.; Jiang, Y.; Liu, S.; Zhao, Z.; Li, R.; Chen, X. ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction. Electronics 2025, 14, 4697. https://doi.org/10.3390/electronics14234697

AMA Style

Jiang S, Wang B, Zhang X, Jiang Y, Liu S, Zhao Z, Li R, Chen X. ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction. Electronics. 2025; 14(23):4697. https://doi.org/10.3390/electronics14234697

Chicago/Turabian Style

Jiang, Shuo, Boyu Wang, Xuan Zhang, Yaoxian Jiang, Shuyi Liu, Zhenyu Zhao, Ruide Li, and Xiao Chen. 2025. "ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction" Electronics 14, no. 23: 4697. https://doi.org/10.3390/electronics14234697

APA Style

Jiang, S., Wang, B., Zhang, X., Jiang, Y., Liu, S., Zhao, Z., Li, R., & Chen, X. (2025). ObsBattery: Position-Aware Federated Learning with Dueling DQN Clustering and Training Adaptation for Satellite Battery Prediction. Electronics, 14(23), 4697. https://doi.org/10.3390/electronics14234697

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