AI-Powered Workflow for Predictive Eclipse Preparation and Energy Budgeting

Automates the repetitive, high-frequency task of calculating upcoming eclipse times and durations for each satellite in the constellation using orbital propagators (e.g., Orekit, GMAT). This replaces manual spreadsheet analysis and calendar reviews, freeing flight dynamics engineers for higher-value anomaly investigation and mission design work.

By integrating power system simulators (e.g., System Toolkit - STK) with the eclipse forecast, the workflow models State-of-Charge (SoC) drop and pre-emptively schedules non-critical payloads (e.g., certain imaging modes, experimental payloads) to idle during eclipse periods. This ensures primary revenue-generating instruments remain powered and operational, protecting service-level agreements (SLAs).

Proactive load-shedding and optimized charge/discharge cycles during eclipses reduce depth-of-discharge and thermal stress on lithium-ion batteries. This directly slows battery degradation, a primary lifespan limiter, potentially extending satellite operational life by 6-12 months and deferring the capital cost of a replacement launch.

The workflow enables fleet-level energy budgeting. By understanding the combined eclipse profile and power demand across the constellation, operators can make strategic decisions—like slightly delaying a low-priority task on one satellite to preserve margin for a high-priority task on another—optimizing total fleet throughput without adding hardware.

Unplanned power shortages are a leading cause of satellites entering emergency safe-mode, which requires intensive, around-the-clock engineering support to recover. This predictive workflow acts as a primary guardrail, virtually eliminating brownout-induced safe-mode events and their associated operational recovery costs and service interruptions.

The implementation provides full observability: every forecast, modeled impact, and automated scheduling decision is logged with rationale. This creates a defensible audit trail for regulators and internal governance, proving due diligence in asset management. Approval gates can be configured for major load-shedding decisions, balancing autonomy with human oversight.

This core agent ingests Two-Line Element (TLE) sets or high-precision ephemerides and uses a propagator (e.g., SGP4, Orekit) to calculate precise eclipse start/end times and durations for each satellite over a planning horizon (e.g., 7-14 days). It outputs a structured schedule of energy-deficit events, which is the primary trigger for the entire workflow.

A high-fidelity model of each satellite's power subsystem (solar arrays, batteries, converters, loads). It ingests the eclipse forecast and simulates state-of-charge (SoC) trajectories under different load scenarios. This agent evaluates the energy impact of planned payload activities and identifies which non-critical tasks must be deferred or shed to prevent brownout.

An intelligent scheduler that holds the queue of planned payload activities (imaging, comms). Upon receiving a power risk alert, it re-optimizes the task schedule, moving non-critical, high-power tasks outside of eclipse recovery windows. It respects mission priorities, ground station visibility, and customer SLAs, requiring integration with the primary mission planning system (e.g., AGI STK, custom scheduler).

This safety-critical agent translates high-level decisions (e.g., 'defer task X', 'reduce transmitter power to Y') into validated, satellite-specific command sequences. It checks each command against a model of the current satellite state and a library of flight rules before releasing the sequence to the secure uplink queue. This is the final guardrail against human error in the automated loop.

The centralized observability layer for flight controllers. It visualizes fleet-wide energy forecasts, simulated SoC curves, scheduled task changes, and all automated actions. It provides override controls and approval gates for high-stakes decisions, ensuring engineers maintain situational awareness and governance over the autonomous system. Integrates with Grafana, Kibana, or custom mission control software.

A closed-loop system that compares predicted power behavior with actual telemetry post-eclipse. Discrepancies (e.g., solar array degradation, battery capacity loss) are used to automatically retune the digital twin's parameters. This continuous learning ensures forecast accuracy degrades over the satellite's lifetime, maintaining the workflow's reliability without manual recalibration.

These engineers own the high-fidelity orbital propagators and eclipse prediction models that form the workflow's core data source. They define the accuracy requirements for eclipse timing and duration forecasts, which directly impact the energy budget's safety margins. Their involvement is critical during integration to validate that the automated system's predictions match their manual benchmarks.

This team provides the detailed power system models and constraints (battery depth-of-discharge limits, solar array performance curves) that the AI uses to simulate energy impact. They define the 'rules of engagement' for automated load-shedding or task deferral and are the final approvers for any automated commands that affect power subsystem states before they are uplinked.

Stakeholders who manage the satellite's tasking schedule. The workflow must integrate with their planning systems (e.g., AGI STK, custom schedulers) to identify non-critical payload or communication tasks that can be safely deferred during energy-critical periods. Their buy-in ensures the automation optimizes for mission continuity, not just power safety.

This team builds and maintains the production orchestration layer, likely using frameworks like LangGraph or Prefect. They are responsible for the data pipeline that ingests propagator outputs, hosts the simulation and optimization agents, integrates with command generation systems, and ensures high availability and observability of the entire automated loop.

The end-users who monitor the automated system's decisions. They require a clear dashboard showing forecasted energy budgets, planned automated actions, and override controls. Their operational procedures must be updated to include monitoring this autonomous layer, and they act as the human-in-the-loop for any high-consequence actions or exception handling.

For regulated operators, this stakeholder ensures the automated workflow has a defensible audit trail, clear delineation of accountability, and failsafes that prevent a single point of failure from causing a loss of vehicle. They govern the rollout sequence, typically demanding a shadow-mode pilot followed by a phased implementation with strict kill-switch capabilities.