Predictive Models

Predict outcomes from your battery data. Faster than you'd test for them.

Name: Micantis Battery Analytics Platform
Author: Micantis

Our data scientists partner with your engineers to design custom machine learning models. Cycle life from 20 cycles. Anomaly detection on the line. Ship release decisions. Whatever your team needs to predict, we train on your production data and back the accuracy with a money back guarantee.

Talk to us about a model See ways to apply

How it works

Meet your engineers Define the output and accuracy target.

Train on your data Your cells, your chemistries, your process.

Validate end to end Held out cells your team picks.

Cut test and decision time Faster cell release calls without the QC wait.

Micantis Machine Learning Models in Practice

See how teams put ML to work.

From cycle life prediction to automated teardown classification — real customer engagements with the Micantis data science team.

Ship early without shipping risk.

A power tool manufacturer needed to deliver cells before the full qualification cycle was done. Working with the Micantis data science team, they ranked every cell from the first 20 cycles of cycle life data — shipped the strongest first and hit the deadline, with predictions that held against full qualification.

The situation

A long term contract pulled forward by a customer's launch schedule. The cells were partway through standard cycle life qualification when the manufacturer was asked to ship in a fraction of the planned window. Continuing full QC meant missing the deadline. Shipping blind meant warranty exposure and the chance of field failures the customer would never forget.

What they did

Working with the Micantis data science team, they trained a custom model on the first 20 cycles of cycle life data from historical production runs. The model scored every cell's predicted cycle life and flagged the outliers. Instead of guessing, they had a defensible ranking they could walk through with their customer.

The result

They shipped the highest scoring cells with confidence and held the lower scoring ones for further qualification. Hit the deadline. The predictions tracked actual cycle life closely across the shipment, backed by the Micantis money-back accuracy guarantee. The model is now standard practice for any time critical shipment.

Before 600 cycles to release

After 20 cycles to release

Predictions vs. Actual

Actual Predicted ±95% CI

Auto classify teardowns. Stop reviewing photos by hand.

A cell producer's QC team was bottlenecked by manual teardown image review. Working with the Micantis data science team, they trained a custom computer vision model on years of labeled teardown photos. Now every cell is auto classified pass or fail in seconds, with engineers reviewing only flagged or low confidence cases.

The situation

After abuse and post mortem testing, every cell goes through teardown inspection. Operators photograph each one and tag it pass or fail by visual signature, looking for lithium plating, separator damage, electrode delamination, and similar failure modes. Volume was overwhelming the QC team, and operator to operator consistency was visibly drifting on borderline cases.

What they did

The Micantis data science team trained a custom computer vision model on years of historical teardown images, all already labeled pass or fail by their senior engineers. The model learned the visual signatures of failure modes across multiple chemistries and cell formats.

The result

Every new teardown image is automatically classified pass or fail with a confidence score the moment it's uploaded. Engineers spend their time on the few flagged or low confidence cases instead of clicking through hundreds of routine photos. What used to take a full shift now takes minutes.

Before 8 hours per teardown batch

After 15 minutes per batch

98% classifications matched senior engineer review

Image Classifier Output FAIL · 99.4%

Vent Blown · 99.4%

Ways to Apply ML

Four ways teams put machine learning on battery data.

Trained on your production data. Validated against your spec.

01 — Predict early

Get answers months earlier.

Skip the wait for metrics that take a full test cycle to measure.

e.g. cycle life from 20 cycles, calendar aging without the year long study.

02 — Spot anomalies

Catch the cells that aren't right.

Flag cells whose signatures diverge from the rest of the population.

e.g. formation outliers, process drift, contamination signatures.

03 — Score for your application

Rank cells by your spec, not the datasheet.

Predict performance against your specific use case and bin accordingly.

e.g. supplier batch QC, ship release ranking, application matched binning.

04 — Reach beyond what you tested

Forecast conditions you didn't run.

Extrapolate to environments and loads you couldn't test directly.

e.g. low temperature performance, abuse tolerance, field duty cycles.

How It Works

Engaged with our data science team end to end.

This isn't an off the shelf model you license and pray. Our data scientists work directly with your engineers to define the prediction target, scope the training data, design the features, validate against cells your team picks, and document where the model's confidence breaks down. The Micantis platform is the data foundation — every cycle, every channel, every cell already normalised and indexed.

Data science partnership: hands on engagement, not a vendor relationship
Production data trained: real manufacturing or test runs, not synthetic data
Any input, any output: formation, metadata, environmentals, supplier lots, you decide
Held out validation: tested on cells your team picks, not ours
Continuous calibration: model updates as more of your data lands

01

Scope the prediction together

Our data science team meets your engineers to define the output, the inputs, and the accuracy target.
02

Train on your production data

Models learn from your cells, your chemistries, your manufacturing process.
03

Validate end to end

Held out cells, your spec, our accuracy benchmark, your sign off.
04

Ship with a guarantee

If the model misses spec on validation, you don't pay.

Guaranteed

If our model doesn't meet the agreed accuracy, you don't pay.

We agree to an accuracy target before training, validate on held out cells your team selects, and refund the model fee if we miss it. Period. No fine print, no clawbacks.

Spec written before training

Accuracy target, validation set size, and pass criteria documented up front.

Held out validation

Your team picks the validation cells. We don't see them during training.

Full refund if we miss

Miss the target on validation, full model fee refunded. No partial credit clauses.

Tell us what you'd predict if you could.

30 minute conversation. We'll tell you whether we can build the model and what the accuracy target would be — before any commitment.

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