Battery/Storage Management System

Purpose

Monitor, control, and optimize the operation of all L1-ESS storage subsystems (Li-ion, thermal, fuel cell, supercapacitor) to maximize storage availability, lifetime, and efficiency while enforcing safety limits and coordinating with the power distribution system

Context

L2-ESS-MGMT is the 'brain' of the energy storage system. It continuously monitors the state-of-charge and state-of-health of every storage element, executes optimal charge/discharge scheduling across technologies, enforces thermal and electrical safety limits, and reports unified storage status to L1-CDH for Ark-level power management decisions. The system uses predictive algorithms to forecast degradation, schedule preventive maintenance, and adapt dispatch strategies as storage capacity changes over the 100-year mission. It coordinates with L2-PDM-LOAD for demand-side scheduling and L2-PDM-BUS for source selection.

Principles

• ▸State-of-charge estimation combines coulomb counting, voltage-based methods, and impedance spectroscopy
• ▸State-of-health tracking monitors capacity fade, impedance growth, and self-discharge trends
• ▸Optimal dispatch across heterogeneous storage types minimizes degradation while meeting load demand
• ▸Safety enforcement includes over-voltage, under-voltage, over-current, over-temperature, and ground fault protection
• ▸Predictive maintenance uses degradation models to forecast replacement timing and spare parts needs
• ▸Hierarchical control: cell-level balancing -> module-level protection -> bank-level dispatch -> system-level optimization

Typical implementations

• ▸Spacecraft BMS with cell-level voltage and temperature monitoring (e.g., ISS battery ORU controllers)
• ▸Grid-scale energy management systems (EMS) for hybrid battery/fuel cell/supercapacitor dispatch
• ▸Kalman filter and machine-learning based SOC/SOH estimation algorithms
• ▸Safety interlock controllers with hardware watchdog timers and independent fault detection
• ▸CAN bus or SpaceWire data networks for distributed sensor data aggregation

Lunar considerations

• ▸Autonomous operation: all decisions must be made locally without Earth-in-the-loop (1.3s one-way delay minimum, possible comm blackouts)
• ▸100-year degradation prediction requires conservative models with large safety margins
• ▸Radiation-hardened processors and redundant controllers for fault tolerance
• ▸Thermal management coordination is critical due to vacuum environment constraints
• ▸Must handle multi-decade dormancy of emergency fuel cell reserves without losing track of state
• ▸Self-learning algorithms should adapt to observed degradation patterns unique to lunar environment

Specifications

Interfaces

Decomposes into

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