Simulate & Predict
Project battery behavior forward from early cycling data.
Battery Digital Twin
Use partial cycling data to estimate lifetime, degradation trajectory, and likely failure behavior before a test campaign fully completes, so teams can stop bad runs early and double down on promising ones.
Key outcomes
50 cycles
minimum upload target
Lifetime forecast
returned automatically
Degradation-aware
decision output
What goes in
Inputs this feature expects
- Cycling data exported from battery tests
- Cell metadata and chemistry context when available
- Optional scenario assumptions for downstream planning
What comes out
Outputs your team can act on
- Projected capacity-retention trajectory
- Expected end-of-life timing and degradation view
- A faster signal for go, kill, or reformulate decisions
Workflow
How teams use Battery Digital Twin
01
Load early-cycle data
Bring in the first portion of a test instead of waiting for the full campaign to finish.
02
Fit the digital twin
Lattice Graph estimates long-horizon behavior from the observed cycling signature.
03
Act while the test is still running
Terminate weak candidates early or redirect lab capacity toward the best performers.
Best fit
Where this feature adds the most leverage
- Battery teams trying to shorten qualification loops
- Stopping low-value cycling campaigns early
- Comparing formulations before full lifetime data exists
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