QuaLiProM Project wields AI to address sustainable battery upcycling
New research has been approved to tackle the issue of upcycling lithium-ion batteries for EVs using quantum technology and artificial intelligence (AI).
In an effort to improve sustainability further within the EV industry, new research has been commissioned to deal with the hurdles of upcycling lithium-ion batteries. Dubbed the “QuaLiProM project,” this initiative will explore the creation of a quick, non-destructive method for analyzing the available power and service life of used lithium-ion batteries.
If successful, this research will enable more batteries to be reused for EVs and other industries. Lithium-ion batteries naturally degrade over time, with lowered capacities and more internal resistance. Today, analyzing the State-of-Health (SoH) of any battery requires delicate electrochemical measurements, which take time. Moreover, existing methods are ineffective at detecting localized defects or hotspots. These challenges result in upcycling programs often being classified as non-viable from an economic perspective.
It's a problem recognized worldwide, but the German Federal Ministry of Education and Research (BMBF) has decided to tackle the problem with this brand-new project. The QuaLiProM project relies on quantum sensors made from diamonds and atomic magnetometry to detect and measure the magnetic fields produced by battery cells.
AI can then be used to analyze the data gathered from these magnetic fields to determine whether battery cells are defective or degraded. Deep learning AI methods can then decide if batteries are suitable for recycling in EVs or suitable for second-life uses in other industries.
It's theorized that this non-destructive method can detect everything from the state of a battery’s charge to impurities and product defects. Most excitingly of all, for EV battery producers, the method can be integrated into cell production directly while being substantially faster than traditional methods.
Several partners have announced their participation in the project, including the Fraunhofer Institute for Manufacturing Technology and Advanced Materials (IFAM), Nehlsen, and Industrial Dynamics.
Expertise has been drawn from several sectors to give the project the best chance of success, with specializations including data analysis and quantum sensor development.
Currently, the timeline for the project is November 30th, 2026. If successful, it could be the end of the decade when we see these methods appear on the ground.