Aggregated State-of-Charge Estimator
Multi-Technology SOC Fusion via EKF/UKF/Coulomb Counting
Centralized state-of-charge estimator that fuses sub-system reports (battery EKF SOC, fuel-cell reactant inventory, supercap voltage, thermal storage temperature) into a unified Ark-level energy availability number, normalized across heterogeneous storage technologies.
Purpose
Provide L3-CDH-AUTO with a single 'how much energy is available?' number for autonomous dispatch decisions over hours-to-weeks horizons, accounting for technology-specific availability rates and round-trip efficiencies.
Context
Aggregates from L3-ESS-LITH-BMS (battery SOC), L3-ESS-FC-CTRL (H2/O2 inventory), L3-ESS-SCAP-CTRL (capacitor charge), and L3-ESS-THRM-CTRL (thermal storage state). Cross-checked against L3-ESS-MGMT-SOH for capacity de-rating. Fed to L3-ESS-MGMT-DISP for scheduling and L2-CDH-AUTO for goal-level decisions.
Principles
- ▸Heterogeneous SOC normalization: each storage technology has different SOC definition (Wh, kg, K) — fused into common 'effective stored energy' units
- ▸Extended Kalman Filter (EKF) tracks battery SOC with 0.32–1% error per recent (Dec 2024) BMS literature, vs. 5% for traditional coulomb counting alone
- ▸Fuel cell inventory SOC = (H2 mass × LHV × stack efficiency) — simpler but conditioned on operating temperature
- ▸Supercap SOC = ½ C V² (energy stored in capacitor)
- ▸Thermal storage SOC = m × cp × (T – T_baseline) for sensible storage, m × Hfusion for PCM
- ▸Bayesian sensor fusion combines multiple SOC sources with uncertainty quantification
Typical implementations
- ▸Recent (Dec 2024) advanced BMS designs: EKF with 0.32–1% error margin
- ▸Adaptive Unscented Kalman Filter (AUKF) for joint SOC/SOH estimation
- ▸NASA cFS battery management software components
- ▸ISS battery SOC fusion (multi-string aggregation)
- ▸Terrestrial microgrid management systems (e.g., Tesla Powerwall fleet management)
Lunar considerations
- ▸Temperature has large impact on Li-ion SOC accuracy; thermal model must be coupled with SOC algorithm
- ▸Replacement modules entering with unknown initial SOC require gradual EKF convergence (~10 cycles)
- ▸Sensor drift over decades (PT1000, current shunts) must be calibrated periodically against absolute references
- ▸PCM thermal storage SOC has hysteresis near phase boundary — track melt fraction explicitly
- ▸Long-mission decay: cumulative coulomb counting error must be bounded via full-cycle calibration events
Specifications
Functional
| primary function | Estimate aggregated Ark energy storage state-of-charge in unified units |
| inputs | Battery SOC from L3-ESS-LITH-BMS, Fuel-cell reactant inventory from L3-ESS-FC-CTRL, Supercap state from L3-ESS-SCAP-CTRL, Thermal storage state from L3-ESS-THRM-CTRL, Bus voltage/current telemetry from L1-PDM |
| outputs | Aggregated SOC (energy units, e.g., kWh-equivalent), Per-technology SOC breakdown, Uncertainty estimate (1-sigma), Forecast envelope for next 24/168 hours |
| update rate hz | 1 |
| soc accuracy kwh | 0.5 |
| soc uncertainty percent at 24h | 5 |
| supported technologies | Li-ion, RFC, supercap, thermal |
| fusion algorithm | Bayesian sensor fusion with per-technology EKF priors |
Physical
| mass kg | 0.0 |
| dimensions | Software on L1-CDH host |
| materials | Code on rad-hard NOR flash |
| operating temperature c | -40, 70 |
Operational
| power consumption w | 1 |
| thermal range c | -40, 70 |
| lifetime years | 100 |
| mtbf hours | 500000 |
Interfaces
Provides
- Aggregated SOC and uncertainty for dispatch planning
- Energy availability for autonomous goal selection
Requires
- Battery SOC and uncertainty
- Reactant inventory and predicted yield
- Supercap state
- Thermal storage state
Cite this entry
Lunar Ark Codex. "Aggregated State-of-Charge Estimator" (L3-ESS-MGMT-SOC). Retrieved 10 September 2026, from https://lunarark.com/entry/L3-ESS-MGMT-SOC
Licensed CC-BY-SA 4.0. You may reuse and adapt this entry with attribution, under the same licence.