For battery R&D & electrochemistry

From cycler data to a trusted battery model

Clean Arbin, Neware, Maccor and BioLogic exports without flattening the electrochemistry, get a System Passport of the experiment — and compile an executable degradation/dynamics model, verified in free-run, with a Nonlinearity Passport. One platform, prepaid, no seats.

Check a cycler file — free, in your browser Read the cleaning guide
Do-no-harm cleaning System Passport per experiment Free-run verified models

Free · runs in your browser

Battery Data Doctor — check a cycler file now

A screening check that runs entirely in your browser: the file is never uploaded and no account is needed. It maps your columns, checks time integrity, completeness, frozen channels and jumps, detects charge / discharge / rest segments — and plots the raw, unsmoothed dQ/dV, so you can see what noise does to it before any cleaning.

Drag a cycler CSV/TSV here or

Runs locally — your data never leaves the browser. For large files, the first ~32 MB are analyzed as a sample.

Why it matters

dQ/dV is why generic smoothing is dangerous

Differential capacity turns charge/discharge plateaus into peaks tied to electrochemical transitions — and numerical differentiation amplifies noise. The field's best practice is explicit: Dubarry & Anseán warn that smoothing can move dQ/dV peaks, and that «smoothing after derivation … should be avoided» (Frontiers in Energy Research, 2022). A cleaner that over-smooths quietly rewrites your electrochemistry.

Do-no-harm by design

When there is nothing to remove, the cleaning backs off to the raw signal instead of over-smoothing — RMSE at the input floor on near-clean channels. Abstention is printed, not hidden: an exact 0.0 dB means the engine declined to touch the signal. See the claim policy.

Check it yourself

Overlay before/after, confirm the fade curve and the dQ/dV peaks. The workflow — including per-cycler units, format gotchas and step-boundary rules — is in the step-by-step cycler cleaning guide.

Three ways in

Start with one file. Grow to the model

Deliverables

What a battery team gets back

ArtifactWhat it contains
Cleaned channelsPer-column denoise, de-spike and gap fill under a do-no-harm criterion — as a clean CSV or over the API.
Anomaly timelineNamed events with attribution: outlier, level shift, regime change, frozen channel, degradation — with per-channel health.
System PassportRecovered per-channel equations with confidence intervals, channel verdicts, the unrecoverable list, and a held-out certificate.
Model artifactmodel.json, Nonlinearity Passport, manifest, checksums and a free-run rollout report — verifiable, hand-off ready.

Why cycling data fits

Built for how battery labs actually work

Synchronized channels, known protocol

Cycling data is synchronized multichannel time series — current, voltage, temperature, protocol state — with the drive recorded alongside the response. No plant hookup is needed: upload what your cycler already exports.

Repeatable, offline, checkable

Experiments repeat, so results can be evaluated offline on held-out cycles — and every published number follows the claim policy: executed runs, declared abstention, stated support boundaries.

Hand-off is the point

Models move on — to BMS work, simulation and reports. Artifacts ship with a Passport, manifest and checksums, so the receiving team can verify what they got instead of taking it on faith.

Start

Try it on your cycling data

Free tier, no card: a fixed starter volume of processing, the full analytics surfaces, and one demo model compile on a public dataset. Or challenge us: send a cycling case — we run it against open baselines and publish both numbers, win or lose.