Upcycling How quantum technology and AI determine the age of lithium-ion batteries

Can used EV batteries be efficiently and safely reused? Upcycling has faced technical and economic challenges, but researchers now offer a ready-to-use solution with AI and high-speed measurement technology.

To enhance the sustainability of electromobility and optimize resource use, upcycling lithium-ion batteries is gaining importance. Rather than immediately recycling used EV batteries, repurposing them in new applications can extend material cycles and conserve resources. However, technical and economic challenges have hindered widespread adoption. The "QuaLiProM" research project, funded by the German Federal Ministry of Education and Research (BMBF), aims to overcome these barriers. An interdisciplinary team is developing a fast, non-destructive, and reliable method to assess the residual capacity and lifespan of used lithium-ion batteries, enabling their safe and economically viable second use.

How healthy is a battery?

Lithium-ion batteries age both during storage and in operation. This becomes noticeable through a loss of capacity and an increase in internal resistance. As a result, energy and performance decrease continuously. The state of health of a battery is usually defined by the State-of-Health (SoH), which describes the age-related change in the state of a cell in relation to its original state. Determining the SoH is a key factor in assessing the performance and service life of batteries.

The SoH of batteries can be determined using various experimental methods. Electrochemical measurements such as capacity tests, electrochemical impedance spectroscopy or service life tests can be used to determine the available residual capacity or the internal resistance of aged cells, for example. However, these are not very meaningful without reference to the initial values of the cells in new condition. Furthermore, electrochemical characterization requires electrical contacting of the cells and is therefore not suitable for rapid diagnostics. In addition, this type of testing only provides information on the global condition of the cell, while defects or charge hotspots cannot be clearly identified

Quantum magnetometry is considered promising

In contrast to the experimental methods previously used for quality control or residual value analysis of lithium-ion cells, quantum magnetometry enables the health status of battery cells to be determined quickly, cost-effectively and precisely. In the field of battery research, it has already been demonstrated that this method can be used to precisely determine the condition-dependent magnetization of a battery cell. In particular, it has been shown that defects, impurities and the state of charge can be detected using quantum sensors. Based on these promising results, a high-speed measurement method based on quantum magnetometry and artificial intelligence is being used as part of the "QuaLiProM project". This will allow battery cells to be classified according to their state of health in industrial applications.

Precisely determine battery condition with rapid test methodology

To develop the rapid test methodology, lithium-ion cells are subjected to forced degradation in the "QuaLiProM project" using cyclic ageing tests. The analysis of the electrochemical measurement data forms the initial data basis for identifying dominant ageing mechanisms. The data is recorded and evaluated. This allows precise conclusions to be drawn about the condition and remaining performance of the cells.

The battery cells that have been transferred to defined ageing states using the ageing tests are then examined using quantum magnetics. The quantum sensor measures the magnetic field of the cells with high precision by observing the spin of a special defect in a diamond, which emits different numbers of light particles depending on the magnetic field. In this way, magnetic field mappings are generated that provide valuable information about possible anomalies in the battery cells. This non-destructive method does not require time-consuming charging and discharging cycles and is therefore suitable for use in cell production as well as in the recycling or upcycling process. The upcoming transfer of the methodology from the laboratory level to the industrial scale is one of the main objectives of the project.

Promoting the sustainable and resource-efficient use of battery cells

For the AI-based analysis of the magnetic field mappings, innovative deep learning methods are used to identify characteristic features, so-called healthy features, which show a clear correlation to the ageing state of the cells. These features are used to classify cells according to their state of health, e.g. healthy, degraded or defective. In this way, degraded but still functional cells that are no longer suitable for use in electric vehicles due to insufficient capacity can be identified. By developing suitable upcycling strategies and researching new second-life applications in less demanding areas, the project aims to promote the sustainable and resource-efficient use of battery cells and accelerate their transfer to industry.