Proposal: Estimating State-of-Charge of a Sunamp Thermino Using OpenEnergyMonitor Energy Accounting
Overview
I’d like to explore whether an OpenEnergyMonitor-based system could be used to estimate the state-of-charge (SoC) — expressed as % usable capacity — of a Sunamp Thermino thermal storage unit by performing continuous energy accounting.
Because the Thermino uses phase-change thermal storage (PCM), internal temperature is not a reliable indicator of remaining stored energy. Instead, the proposal is to model stored energy by measuring energy flows in and out of the unit and applying a self-correcting estimation loop.
I’m looking for feedback on feasibility, measurement strategy, error sources, and implementation ideas.
Core Concept
The model treats the Thermino as an energy reservoir:
Stored Energy ≈ Energy In − Energy Out − Losses
The proposed system would:
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Measure electrical charging energy
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Monitor the Thermino heater supply using an OpenEnergyMonitor power meter.
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Integrate overnight charging energy.
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When charging stops, treat this as a known “full” reference state.
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Measure hot water energy draw
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Use a flow sensor plus inlet/outlet temperature sensors.
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Calculate thermal energy delivered:
Energy = flow × specific heat × ΔT
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Subtract this from stored energy.
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Model standby losses
- Apply a simple loss model based on elapsed time and ambient temperature.
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Estimate state-of-charge
- Convert remaining modeled energy to % of nominal capacity.
Self-Correction Mechanism
To reduce drift:
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After each charge cycle, compare modeled remaining energy with actual measured recharge energy.
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Use this difference to gradually adjust calibration parameters.
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Apply smoothing/averaging to avoid overcorrection due to sensor noise.
This creates an adaptive energy model that improves over time.
Why This Approach?
PCM thermal storage behaves differently from conventional hot water tanks:
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Internal temperature remains relatively constant during phase change.
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Temperature alone does not indicate stored energy.
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Energy flow accounting is likely the most viable external estimation method.
This mirrors approaches used in battery fuel-gauging systems.
Expected Accuracy
Realistically:
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Sensor error and thermal losses will introduce drift.
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With calibration and correction, an estimated ±5–10% SoC accuracy may be achievable.
This would be sufficient for monitoring, optimisation, and automation — not laboratory precision.
Proposed Hardware
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OpenEnergyMonitor power measurement on heater supply
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Flow sensor on hot water outlet
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Temperature sensors (inlet/outlet)
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Optional ambient temperature sensor
Software Implementation Ideas
Potential platforms:
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emonCMS processing pipeline
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Home Assistant integration
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Custom Python or Node-RED logic
Core loop:
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Detect charge cycle → reset reference
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Integrate energy flows
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Apply loss model
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Update SoC estimate
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Apply drift correction
Open Questions / Feedback Requested
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Best flow sensing options for domestic hot water accuracy?
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Has this been done and am I reinventing the wheel ?
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Recommended temperature sensor placement strategy?
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Approaches to modeling standby losses?
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Calibration methods to reduce long-term drift?
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Existing emonCMS workflows that could support this?
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Practical pitfalls others have encountered measuring thermal energy flows?
Goal
The aim is to build a practical “thermal battery fuel gauge” that provides meaningful insight into remaining usable hot water energy, enabling smarter energy management and system optimisation.
I’d really appreciate community feedback on feasibility, measurement techniques, and implementation strategy before moving to a prototype stage.