不可见的空间计算成本：通过物理驱动建模量化近地轨道电池老化
Unseen Cost of Space Computing: Quantifying LEO Battery Aging via Physics-Driven Modeling
AI总结：
本文通过物理驱动建模量化近地轨道卫星电池老化成本，提出退化感知调度策略以延长系统寿命。
摘要：
低地球轨道（LEO）卫星星座在6G时代正演变为智能在轨计算平台，形成空间计算电力网络（SCPN），以提供全球规模的计算服务。然而，SCPN内的高强度计算带来了一个显著的『不可见成本』：频繁的充放电循环加速了卫星寿命限制和高成本电池的物理退化，从而威胁此类系统的长期运营可行性。现有方法通常依赖间接指标如放电深度（DoD），并忽视电池老化复杂非线性退化过程，无法准确量化此成本。为此，我们引入了一个高保真度的物理驱动模型，将计算负载参数与电池退化非线性过程定量联系起来。基于此模型，我们制定了一个退化感知调度问题，并分析了不同能量区域下的启发式策略。模拟结果表明，最优策略应具有适应性：在太阳能丰富的条件下，最大化瞬时太阳能利用的短视策略更优，而在能量匮乏时，利用实时电池状态的反应策略显著延长寿命。
英文摘要：
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.
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