Technology | Limatica

It's time for a new quality measure

Other measurement techniques either take up too much time or do not offer the required data granularity. LIMATICA has developed a novel approach and uses voltage noise to predict self-discharge.

How it works

Voltage noise reveals the cell’s intrinsic activity – like a stethoscope for batteries.

1. Capture

Proprietary, high-resolution hardware measures new data on voltage noise

2. Understand

Make voltage noise data accessible by understanding self-discharge patterns

3. Diagnose

Patented analytics to predict self-discharge in 15minutes

4. Scale

Same precision from lab to gigafactory proven through customer projects

Fast

Results in <1h creating necessary feedback-loops

Scalable

Cost-efficient HW & short test enable giga application

Safe

Passive measurement w/o external stimulation

Chemistry-agnostic

Proven with multiple cell formats and chemistries

Data-rich

Advanced analytics enabling root-cause detection

Easy integration

Can be integrated into existing prodiuction lines

Challenges in battery manufacturing

1 / Growing demand

The battery demand is constantly rising and remains a mega trend. By 2030, nearly 5.000 Gwh will be required through respective demand - production capacity even higher.

2 / Long aging times

The aging process is one of the final production steps and serves as quality assessment. This step is however particulartly time-consuming, with a total of 5 to 28 days of dead time.

3 / High production scrap

Especially during ramp up, scrap rates often exceed 50%. With a qualification step of >5 days, necessary feedback loops are taking too long while production continues.

4 / Lack of data

The status quo is a basic self-discharge measurements. To detect this effect multiple days/weeks are necessary and only limited amount of data is received.