Fair Dinkum Systems
Revolutionizing Battery Life Prediction
Battery Lifecycle Management
The Future of Energy is Electric
Batteries are at its Heart
- Global battery demand has grown 33% annually for three decades, with electric vehicles and autonomous systems driving even stronger future growth
- Battery chemistry's complex, non-linear behavior makes it ideal for deep learning applications
Global battery demand has grown 33% annually for three decades, with electric vehicles and autonomous systems driving even stronger future growthWe're building the next generation of battery lifecycle management software. Our SaaS platform (in development) empowers manufacturers to precisely forecast battery Remaining Useful Life (RUL) and perform Impedance Matching using cutting-edge machine learning.
Using a well known battery charging dataset our algorithm accurately predicts the RUL of LFP/graphite cells with just 15 charging cycles of data.
Our Technology
Battery Life Prediction
What this means in practice: We can predict remaining battery life within 15 cycles using 85% less data than traditional approaches. This enables faster quality control, reduced testing costs, and more efficient battery development.
We're now partnering with battery manufacturers to demonstrate these capabilities at commercial scale. Together, we're building a more sustainable and efficient energy future.
Our Team
Meet Our Team
Our team of experts is dedicated to pushing the boundaries of AI and delivering innovative solutions.
Louka Ewington-Pitsos Chief Operating Officer
Michael Reitzenstein Principle Data Scientist 
Victor Goh Research Scientist 
Alan Huynh Machine Learning Engineer 
Lucas Rose-Winters Backend Developer