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Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling

Li Zeng, Jingyang Zhu, Zixin Wang, Yuanming Shi, Khaled B. Letaief

Abstract

Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliver global-scale computing services. However, the intensive computation within SCPN incurs a significant ``unseen cost'': the frequent charge-discharge cycles accelerate the physical degradation of satellites' life-limiting and high-cost batteries, thereby threatening the long-term operational viability of such a system. Existing approaches, often relying on indirect metrics like Depth of Discharge (DoD) and neglecting the complex, nonlinear degradation process of battery aging, fail to accurately quantify this cost. To address this, we introduce a high-fidelity, physics-driven model that quantitatively links computational workload parameters to the nonlinear battery degradation. Building on this model, we formulate a degradation-aware scheduling problem and analyze heuristic policies across different energy regimes. Simulations reveal that the optimal strategy should be adaptive: in solar-rich conditions, a myopic policy maximizing instantaneous solar utilization is superior, whereas under energy scarcity, a reactive policy leveraging real-time battery state significantly extends lifetime.

Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling

Abstract

Low Earth Orbit (LEO) satellite constellations in the 6G era are evolving into intelligent in-orbit computational platforms, forming Space Computing Power Networks (SCPNs) to deliver global-scale computing services. However, the intensive computation within SCPN incurs a significant ``unseen cost'': the frequent charge-discharge cycles accelerate the physical degradation of satellites' life-limiting and high-cost batteries, thereby threatening the long-term operational viability of such a system. Existing approaches, often relying on indirect metrics like Depth of Discharge (DoD) and neglecting the complex, nonlinear degradation process of battery aging, fail to accurately quantify this cost. To address this, we introduce a high-fidelity, physics-driven model that quantitatively links computational workload parameters to the nonlinear battery degradation. Building on this model, we formulate a degradation-aware scheduling problem and analyze heuristic policies across different energy regimes. Simulations reveal that the optimal strategy should be adaptive: in solar-rich conditions, a myopic policy maximizing instantaneous solar utilization is superior, whereas under energy scarcity, a reactive policy leveraging real-time battery state significantly extends lifetime.
Paper Structure (21 sections, 12 equations, 4 figures, 1 table)

This paper contains 21 sections, 12 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Architecture and Orbital Dynamics
  4. Satellite On-Board Energy Harvesting Model
  5. Eclipse Condition
  6. Panel Orientation and Cosine Loss
  7. Battery Degradation Model for Computation Tasks
  8. Battery Cycle Life Model
  9. Instantaneous Degradation Rate
  10. Total Degradation from a Computational Task
  11. Standard Formulation of Single Task Scheduling with Degradation
  12. Simulation Study
  13. Simulation Setup
  14. Constellation and Satellite Parameters
  15. Task Generation and Experiment Design
  16. ...and 6 more sections

Figures (4)

  • Figure 1: System model overview. A satellite's power source for a computational task depends on its orbital position. In the sunlit hemisphere, it primarily uses harvested solar power, whereas in the eclipse hemisphere, it relies solely on its battery, which accelerates degradation.
  • Figure 2: Average degradation cost under two different energy harvesting regimes.
  • Figure 3: Impact of task workload on the average degradation cost for a fixed time budget.
  • Figure 4: Impact of the time budget on the average degradation cost for a fixed workload.

Theorems & Definitions (1)

  • Remark