We predict lithium-ion batteries complete lifetime

Providing stability and control

Who we are

We provide a data driven machine learning solution with the purpose to predict lithium-ion batteries (complete) lifetime from 1st life to end of life.

Who

Sustainability, commitment, innovation, idea and joyfulness.

Why

Creative thinking for the planet.

How

Sustainability and control.

What

Service that provide savings, but also understands the lifetime of the battery.

The AiTree Cloud solution

We use the data from the first life lithium-ion application and combine that with our inhouse developed algorithm to calculate the lithium-ion batteries complete lifetime.

The benefits with AiTree Solution
• Create next generation of smart, safe and cost-effective lithium battery solutions.
• Understand the scenario with re-manufacturing.
• Analyse data and optimize the battery size to better understand the lifetime. 1st life? 2nd life? End-of-life and recycling?
• Significant reduced test and verification.
• ID tracking for reporting to authorities.

= That gives you stability and control.

Interested to know more? Drop us an email at predict@aitreetech.com

Introduction

“There is no question that the electric vehicle has a great future”. Surprisingly, it is not Elon Musk who is credited with these words, but W.C. Anderson, president of an electric car company, a little over a hundred years ago. Unfortunately, they were uttered a bit too early, in 1912 – at the same time as Ford was breaking sales records with its Model T, mobilizing the masses at an unbeatable price of 650 USD (roughly 17000 USD today). The future of electric vehicles looked bleak as the world embraced fossil fuels. And now ours does as well, for very similar reasons. The incredible progress in mobility needs to be put on a sustainable foundation for us not to go the way of Mr. Anderson’s products.  Of course, it would help not to repeat the mistakes of the past – after all, it was the energy density and abundance that made oil the dominant energy source. Which brings us to batteries. 

The wind of change

Kickstarted by the need for portable electronics such as phones and laptops and fueled by the increasing demand in storage capacity, battery technology has seen unprecedented improvements over the past decade.  Nevertheless, even the most advanced battery types share a common trait – they degrade over time, decreasing the amount of potential energy (or capacity) they can store. After they drop to about 80 % of their nominal capacity – typically after several thousand cycles – they are considered at the end of their so-called remaining useful life (RUL) and usually discarded. But the end of RUL does not have to mean retirement. Batteries are prime candidates for second-life applications in systems featuring lower requirements, stationary applications or auxiliary units. But how can we determine how long the battery has left until absolute failure? After all, any investment in cycling applications requires a safe assessment of remaining capacity and projected degradation trajectory. Enter another promising field experiencing rapid growth – machine learning. 

The AiTree – proof of concept

One can argue that the most crucial part of machine learning is acquiring well-annotated data. Even the most complex model will not be able to make sense of a dataset that is too small, missing key variables or of poor quality. Without good quality data, the model choice or parameter tuning is of little importance. The team behind “Data-driven prediction of battery cycle life before capacity degradation” (Severson et al., 2019) provides an extensive dataset monitoring battery degradation. In their paper the authors claim the dataset to be the largest publicly available dataset so far, containing close to 97,000 charge-/discharge cycles of 124 commercial LiFePO4 battery cells.  

During the charge-/discharge cycles, the team continuously monitored voltage, current, temperature and internal resistance of the batteries. External factors influencing battery degradation were limited during the data generation process by performing measurements in a temperature-controlled environmental chamber set to 30 °C.  The batteries were subjected to varied fast-charging conditions to facilitate different degradation rates while keeping identical discharge conditions.   

Career

Our team is our most vital asset. Our mission is to deliver creative thinking for the planet! Since we put allot of effort in our team, we made it easy for you to send in an application. Please read below statement. If you honestly feel the same as we do, then you might be our next colleague to join our mission.

Do you feel a strong desire to deliver creative thinking for the planet?
Interested in machine learning?
Do you have the passion for renewable energy?
Do you like agile development?

Please send the application to teamup@aitreetech.com

Kind regards
The AiTree Tech team

Partner

We believe in  partnership, we think that is the only way to actually save the planet. AiTree Technology offers creating thinking for the planet. Therefore we are happy to have the partners below signed to our network.

   

 

Interested in our partner network? Please drop us a mail at: partner@aitreetech.com

Kind regards
The AiTree Tech team

Investors

The niched competence inside AiTree Tech gives us strong competitive advantage. AiTree Tech vision to deliver the AiTree Cloud solutions on a global level is bold but fully reachable. Therefore AiTree technology is always open to discuss strategic routing. Do you have the competence to scale up a company to a global level and/or the network that can give us quicker access to an international entry? Then you might be the investor we are currently looking for.

Please drop us a email at board@aitreetech.com

Kind regards
The Board of directors for AiTree Technology

Visit us!

Address

Järntorget 4
413 04 Gothenburg

Map

Contact

Phone

+46(0)733-80 08 44