Batteries: from cycler files to a ranked, evidence-labelled shortlist
The public mirror of the Batteries depth studio. Cycle-life fits and transport mixing are computed, remaining-useful-life forecasts are predicted with an interval, and missing data is labelled — never dressed up as a measurement.
What batteries teams come here to do
Each job maps to a module that exists — the links go straight into the platform.
- Parse vendor cycler files — Bio-Logic, Arbin, Neware and Landt exports become structured, evidence-tagged rows instead of retyped spreadsheets (/instruments).
- Fit capacity fade and forecast RUL — A semi-empirical fade model with a temperature term returns remaining useful life with a split-conformal interval at the 80% end-of-life threshold (/batteries/depth).
- Screen multi-component electrolytes — Mixture viscosity, density and conductivity from component data — or a labelled group-contribution estimate when measured conductivity is missing (/studio/batteries).
- Balance life, cost and footprint — Search compositions and conditions with cycle life, material cost and GWP as objectives rather than afterthoughts (/studio/optimization, /studio/lca).
- Close the loop — Log validation runs and feed measured results back into the model before the next campaign (/studio/bench).
The real pieces behind this workflow
No roadmap items and no imagined features: every card opens a shipped module route.
Cycle-life fit + RUL with intervals, electrolyte transport mixing, candidate screening and aging analytics in one evidence-tagged workspace.
Multi-component electrolyte design with transport models, voltage windows and aging analytics on the core batteries page.
Auto-detect and parse battery-cycler vendor files into structured rows — the raw input the depth fit needs.
Constrain composition and bounds, then rank candidates on capacity, cycle life and material cost together.
GWP, cumulative energy demand, E-factor and an ESG score per candidate, under a stated boundary.
What the outputs can honestly support
Screening-grade where true; engine-labelled everywhere.
Questions before you start
Including the limitations we would rather state now.
What data does Matflow need to fit cycle life?
Is the RUL a warranty?
Does Matflow run real battery physics?
Can I screen electrolytes without measured conductivity?
Where do my files live?
Start with a cycler file
Upload a Bio-Logic, Arbin or Neware export, review the parsed rows, and fit your first degradation curve — free tier, no card.