We publish our method because a forecast that cannot be audited is not much use for a decision. This page sets out what we observe, how it becomes a degradation trajectory, how we validate it against measured outcomes, and — just as important — what this approach cannot tell you.
A battery reveals how it is being used through how it operates in its market. That behaviour — not a datasheet — is what determines how it ages, and it can be characterised at fleet scale.
Lithium-ion ageing follows mechanisms documented over two decades: calendar loss driven by time, temperature and resting state of charge, and cycling loss driven by the depth and frequency of each cycle.
Generic literature parameters are a starting point. Every additional asset, chemistry, climate and vintage in the corpus sharpens the prior for all the others. Accuracy is a function of coverage.
We continuously assemble what is knowable about each asset: how it charges and discharges, what services it provides, its configuration and chemistry, the climate it sits in, and — for clients — its own plant telemetry. Every input is versioned with its provenance and confidence.
Power flows become an energy state trajectory: a state-of-charge profile, from which we derive equivalent full cycles, throughput, effective C-rate and time spent at high state of charge — the variables ageing actually responds to.
Rainflow counting — the fatigue-analysis standard adapted to batteries — decomposes the profile into individual cycles and their depth. Depth distribution matters enormously: one deep cycle damages far more than several shallow ones moving the same energy.
The fingerprint drives a calendar + cycling ageing model parameterised by chemistry, with Monte Carlo propagation of parameter uncertainty. Output: a state-of-health trajectory with p05–p95 band, remaining life as a range, and augmentation timing.
Where measured outcomes exist — capacity demonstrations, accredited capacity, published fleet performance, client capacity tests — the model is calibrated against them. Bands narrow as evidence accumulates, per asset and across the fleet.
We use the semi-empirical family that constitutes the academic and industrial standard — the same foundation used by national laboratories and the leading vendors:
Methodological references: ageing models after Xu et al. (IEEE Transactions on Smart Grid) and Schmalstieg et al. (Journal of Power Sources, 2014); rainflow counting and depth-of-discharge stress functions after Shi et al. (University of Washington, 2017); chemistry parameters cross-checked against NREL's open-source BLAST-Lite. Full assumptions accompany every report.
| Published evidence | Value |
|---|---|
| ERCOT fleet (Modo Energy, 2025) | ≈7% / 365 EFC |
| Great Britain fleet (Modo Energy, 2024) | ≈4.4% / 365 EFC |
| Utility BESS, Italy — 3 years (Energies, 2026) | ≈1.4% / year |
| Grid-scale LFP, literature (first decade) | 2–3% / year |
| Peak shaving vs frequency regulation | ≈1.8× degradation |
| BESS failure incidents 2018→2025 (EPRI) | rate −99% |
The ERCOT-versus-Britain gap is the point: two fleets of similar technology ageing at rates that differ by 1.6× because they are operated differently. Degradation is a behavioural outcome, and behaviour is measurable.
Six independent classes of evidence. No single one is sufficient; together they are what makes a forecast defensible.
How storage assets are actually charged and discharged across the world's power markets, at the finest resolution each market allows — the basis for every cycling fingerprint and the only way to know how an asset is truly being used.
Capacity, duration, chemistry, cell and container model, integrator and commissioning date. Without this layer a time series is uninterpretable — degradation modelling is cohort modelling, and the cohort is defined here.
Ambient and site thermal history at hourly resolution anywhere on earth. Temperature is the second driver of calendar ageing after time, and it is why an identical system ages differently in Atacama, Texas and Scotland.
Capacity demonstrations, accredited and de-rated capacity, published fleet performance, incident and failure records, and client capacity tests. This is the ground truth that turns a model into a validated model.
Laboratory cycling and ageing datasets, peer-reviewed degradation models and open-source reference implementations, spanning chemistries from LFP and NMC to sodium-ion and LMFP.
BMS exports, meter data, temperatures, capacity test results and configuration. The layer that converts a well-founded estimate into a measurement of your asset — and the one that narrows the band the most.
This is why the service is progressive. We tell you what we can see, and we label it as what it is. Your data turns an estimate into a measurement. We never present the first as the second.
We will walk through the pipeline, the assumptions, the references and a reproducible worked example on an asset of your choosing.
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