Insider Brief:
- Researchers at the Fraunhofer Institute developed a quantum technology and AI-based method to assess the viability of second-life lithium-ion batteries, enabling faster and non-destructive evaluation.
- The QuaLiProM project integrates atomic magnetometry with deep learning algorithms to classify battery cells based on their aging state, detecting defects and charge inconsistencies more precisely than traditional electrochemical tests.
- The initiative intends to scale quantum sensor technology for industrial applications, making battery diagnostics cost-effective and improving quality control in production and recycling facilities.
- By optimizing second-life battery assessment, the project seeks to reduce waste, enhance resource efficiency, and support sustainable battery repurposing for electromobility.
PRESS RELEASE — According to a recent announcement from the Fraunhofer Institute for Manufacturing Technology and Advanced Materials, researchers have developed a new method combining quantum technology and artificial intelligence to assess the viability of second-life applications for lithium-ion batteries. As mentioned in the article, this approach is intended to improve battery upcycling by enabling faster, non-destructive evaluation of battery health, addressing both technical and economic barriers to reusing electric vehicle batteries.
The research is part of the QuaLiProM project, funded by the German Federal Ministry of Education and Research. The initiative focuses on determining the State-of-Health of used lithium-ion batteries—a key metric describing battery aging and remaining capacity—without relying on traditional, time-consuming electrochemical tests.
QUANTUM SENSORS FOR BATTERY DIAGNOSTICS
As stated in the article, conventional methods for assessing battery health involve cycle tests and electrochemical impedance spectroscopy, which require direct electrical contact with the cells and provide only a global assessment of battery condition. These methods struggle to detect localized defects, charge inconsistencies, or hidden aging mechanisms.
In contrast, the QuaLiProM project integrates atomic magnetometry with AI to enable rapid, high-precision diagnostics. Quantum sensors, particularly diamond-based nitrogen vacancy centers, are used to measure the battery’s magnetic field, providing detailed insights into internal structural changes, defects, and charge distribution. This method allows for the classification of battery cells based on their aging state—distinguishing between healthy, degraded, and defective cells.
The AI component of the project involves deep learning algorithms trained on magnetic field mappings of aged batteries. These algorithms detect characteristic “health features” that correlate with battery performance and degradation patterns. The researchers intend to scale this process for industrial use by making it suitable for quality control in battery production and recycling facilities.
TOWARDS INDUSTRIAL IMPLEMENTATION
The Fraunhofer Institute for Manufacturing Technology and Advanced Materials is leading the effort to develop AI-driven SoH classification systems. Other project partners include Friedrich-Alexander-Universität Erlangen-Nürnberg, Industrial Dynamics GmbH, and Battery Dynamics GmbH, among others.
The project is focused on adapting quantum sensor technology for large-scale industrial applications, ensuring that second-life battery assessment becomes cost-effective and scalable. By enabling efficient sorting of degraded but still functional battery cells, the initiative will work towards reducing waste, improving resource efficiency, and promotunbg sustainable battery repurposing.
According to the article, integrating quantum sensor-based diagnostics into production and recycling workflows could notable improve the economic viability of battery upcycling. The QuaLiProM project represents an effort to bridge laboratory research and industry implementation, supporting the broader goal of sustainable electromobility.
