Physical AI for lithium-ion batteries

You can see every battery's data.
You can't see what's happening inside it.

Go beyond BMS-reported health with real-world battery intelligence. Get pack and cell-level actual electrochemical health, 4-8 months in advance failure prediction, RUL, and root-cause analysis, with a confidence score for every output.

Zylectra

Physics-informed AI

Which batteries are going

Not fleet claims, not a demo. What the model itself is built and measured on today.

201

cells trained of

LFP chemistry

246K+

cycles analyzed

real charge-discharge history

4–8 mo

failure prediction window

vs. industry’s 4–6 week standard

degradation mechanisms attributed

SEI, LAM, plating, LLI

Battery data is everywhere.
Battery certainty isn't.

Every battery ages differently, and real-world usage changes how it behaves. BMS data and dashboards show you what the battery reports, but they don't tell you its true condition, what will happen next, or why.

Failures arrive too late

Weak batteries often remain in service until they become failures, downtime, or warranty claims.

Batteries are judged by the wrong signals

A BMS reports one number for the whole pack, but the cells inside disagree under real load and heat.

Every battery gets treated the same

Without knowing which pack or cell is truly at risk, healthy batteries get replaced too early while weak ones stay in service too long.

Example read · Pack #482

The pack looked fine.
One cell wasn't.

A pack-level average is a summary, not a diagnosis. Zylectra reads every cell, then explains what it found.

What the BMS reports

94%

Pack state of health

No fault raised

What the cells actually say

41-93%

Spread across 24 cells

Cell 14 sits alone at the bottom

Why it is happening

Plating

Lithium plating on cell 14

Traced to sub-zero fast charging

Same pack. Same telemetry. The difference is how deep the model is able to read, and whether it can tell you why.

Current approach

Battery dashboards

They monitor batteries, display health scores, trigger alerts, and show trends.

Health scoresVoltage & temperatureAlertsDashboardsManual decisions

You get the data. You're still left interpreting it.

With Zylectra

Battery intelligence

Zylectra uses Physical AI grounded in battery physics to turn battery data into actionable intelligence.

Know true pack & cell health
Predict failures months ahead
Estimate remaining useful life
Identify root cause & responsibility
Know how confident the system is

You don't just see what happened. You know what to expect next.

Months of warning.
Down to the cell.

Most battery tools give you a pack-level score and a few weeks of notice. Zylectra predicts earlier, reads deeper, and runs on the data you already collect.

How far ahead a failure is detected

BMS thresholds & dashboards4-6 weeks

Zylectra4-8 months

02 mo4 mo6 mo8 mo

Solid bar shows the low end of the window, the softer extension shows the high end. Industry-standard threshold and trend alarms typically surface a problem 4 to 6 weeks out.

Root cause at pack and cell level

A pack-level score tells you something is wrong. Most tools stop there. Zylectra names the cell, then attributes the loss to the electrochemistry actually driving it.

Mechanism attribution · Pack #482 · Cell 14CONFIDENCE 86 · HIGH

Lithium plating

54%

SEI growth

20%

Loss of active material

18%

Loss of lithium inventory

Attribution is derived from electrochemical modelling, a single particle model and Arrhenius kinetics, rather than pattern-matching on history. Every output carries its own confidence score, so you know how much weight the read can hold.

Nothing to install

Zylectra runs on the telemetry your packs already produce. No hardware programme, no fleet downtime, no waiting on a procurement cycle to find out what your batteries are doing.

New sensors

Gateways or dongles

Vehicle retrofits

One reading.
Five different decisions.

A fleet, a vehicle maker, a cell maker, a swap network and a lender look at the same pack and need five different answers. The physics does not change. What it is worth to you does.

The reading · constant

81%true pack SoH

Pack #482. The BMS still reports 94% and has raised no fault.

Weakest cellCell 14 · 41%

Dominant mechanismLithium plating · 54%

Remaining useful life~5 months

CONFIDENCE 86 · HIGH

Swap at the next service, not on the roadside.

Cell 14 breaches its safe window in 6 to 9 weeks. Planning the swap into scheduled maintenance removes the recovery call and the lost running days, and the packs that are actually healthy stay earning.

₹18 Lper year, 500-pack fleet

35 unplanned failures avoided₹3.5 L

12 premature replacements deferred₹14.4 L

Worked example500-pack LFP fleet · pack replacement ₹1,20,000 · swap revenue ₹2,400 per pack per month · vehicle downtime ₹1,100 per day. Figures are arithmetic on those assumptions applied to one pack read, not results from a deployed fleet.