Who Is This For?
If you make decisions about batteries, we make those decisions better.
Cell Designers
You're choosing silicon content, electrolyte, and cycling protocol. You need to know which design lasts longest — without waiting 6 months for aging data.
Run 1,000 virtual aging tests in 30 seconds. Change one variable, see the impact instantly.
Pack & Fleet Engineers
You're managing batteries in the field. You need to predict when packs will hit 80% capacity so you can plan replacements and set warranty terms.
Model real-world conditions — temperature swings, fast charging, partial cycling — and get a predicted end-of-life date with confidence bounds.
Finance & Operations
You're setting warranty reserves, planning fleet budgets, or evaluating batteries for second-life resale. You need numbers you can put in a spreadsheet.
Get physics-backed lifetime projections that hold up under changing conditions — not just curves from past data that break when conditions change.
What Scale Prognostics™ delivers
Seven coupled degradation mechanisms in a single engine. Every simulation returns a complete diagnostic picture — not just a capacity curve.
Lifetime Prediction
See exactly how your battery will age, cycle by cycle. Our physics engine models seven failure mechanisms working together — not a statistical guess, but a simulation of what’s happening inside the cell.
Know Why, Not Just When
Which mechanism is killing your cell? SEI film growth? Silicon cracking? Transport blockage? Mode decomposition breaks capacity loss into root causes so you can fix the right problem.
Knee Point Early Warning
Batteries don’t always fade gradually — sometimes they fall off a cliff. Our criticality index warns you when a cell is approaching irreversible cascade, before it’s visible in your data.
Real-World Thermal Effects
A battery in Phoenix ages differently than one in Oslo, even at the same average temperature. We model seasonal and daily temperature swings as independent aging drivers.
Built-In Confidence Bands
Every prediction includes an uncertainty envelope. You see the best case, worst case, and most likely outcome — so you can make decisions with the right level of caution.
Millisecond Speed
Full 1,000-cycle simulation in about 30 milliseconds. Run “what if” scenarios, compare designs, and explore tradeoffs interactively — fast enough for a meeting, not just a lab.
Three steps to a full prediction
No coding required. Use our web dashboard, or connect through our API or Python SDK if your team prefers.
Describe your cell
Tell us the basics: silicon content, particle type, charge rate, temperature, and how many cycles you want to simulate.
Run the simulation
Our engine models all seven degradation mechanisms simultaneously — SEI growth, silicon cracking, lithium plating, transport loss, and more.
Get the full picture
See your capacity forecast with confidence bands, a breakdown of which mechanisms caused the loss, and an actionable recommendation.
For developers: Everything available in the dashboard is also accessible through our REST API and Python SDK.
What a simulation tells you
Here’s what it looks like at cycle 800 of a 20% silicon cell at 1C. Every number is backed by physics, not curve fitting. Try it with your own cell →
SIMULATION SNAPSHOT — CYCLE 800Mode decomposition splits total capacity loss into root causes. This is the question every battery engineer asks: “what’s actually killing my cell?”
Tested against the literature
Calibrated against 8 peer-reviewed datasets spanning 2.5%–20% silicon, multiple charge rates, and temperatures from 25°C to 45°C. We show every result — including the hard ones.
| Source | Silicon | Condition | RMSE | Grade |
|---|---|---|---|---|
| Nature Commun. 2021 (LPD) | 20% | 0.5C, 25°C | 0.13% | A+ |
| Kirk et al. 2024 | 10% | 0.5C, 25°C | 0.17% | A+ |
| HPQ GEN3 18650 | 18% | 0.5C, 25°C | 0.18% | A+ |
| Dose et al. 2023 | 8% | 1C, nano-Si | 0.23% | A+ |
| 5% Si Composite | 5% | 1C, 45°C | 0.25% | A+ |
| Nature Commun. 2021 (HPD) | 20% | 0.5C, knee | 1.23% | A |
| LG M50T | 2.5% | 1C, 25°C | 3.87% | C |
| Kirk 2024 Fast Charge | 10% | 2C, fast charge | 1.01% | A |
Grades shown are for calibrated presets (model parameters fitted to each dataset). Out-of-sample accuracy depends on your specific chemistry — use the free trial to test against your own data. We publish every grade because transparency matters.
Built for battery teams
Whether you’re designing cells, writing BMS software, setting warranty terms, or choosing a cooling system — we give you the numbers you need.
Cell Design
Sweep silicon content, particle size, and operating conditions to find the sweet spot between energy density and cycle life — before you build a single prototype.
Battery Management
Generate rich degradation trajectories with 30+ channels to train and validate your BMS. Mode decomposition tells you which mechanism to monitor in the field.
Warranty & Finance
Predict the exact cycle at which capacity crosses 80% end-of-life for any operating envelope. Set warranty terms with physics-backed confidence bands.
Thermal Strategy
Compare passive, forced-air, and liquid cooling. Model real-world seasonal and daily temperature swings. Quantify the lifetime cost of underinvesting in thermal management.
Simple, scalable pricing
A single physical aging test takes 4-8 months and costs $10,000-$80,000 — for one set of conditions. One Starter subscription runs 2,500 simulations per month across any conditions you define.
Annual billing saves 2 months (17% off). Volume discounts available for teams of 10+.