This work presents a new method for automatically determining electrode material combinations in lithium-ion and sodium-ion battery cells. The approach leverages a comprehensive database of half-cell Open Circuit Potentials (OCPs), gathered from both original measurements and literature data of various electrode materials [5]. By reconstructing the OCV curve, as previously demonstrated in various publications [1, 2, 3, 4], of a full cell using all possible electrode material combinations from this database, the algorithm identifies the most likely material pairing based on a fitting error minimization.

The validation of the algorithm was conducted using five battery cells, for which both positive and negative electrode materials were previously identified through Energy Dispersive X-ray (EDX) analysis. This validation step ensures the accuracy and reliability of the proposed method, demonstrating its capability to correctly identify the electrode combinations.

In addition to material identification, the algorithm is capable of determining the stoichiometries of the electrodes. This information could enable further analysis regarding aging mechanisms, particularly focusing on the Loss of Lithium Inventory (LLI) and the Loss of Active Material (LAM) on both electrodes. By analyzing the capacity ratio between the anode and the cathode, the method provides insights into aging processes such as active material degradation. Such capabilities are crucial for assessing the state-of-health and remaining useful life of battery cells, which is of great importance for users.

This approach is non-destructive, fast, and cost-effective, making it suitable for both new and used cells, which is increasingly relevant for battery recycling and second-life applications. The algorithm’s adaptability to different types of cells, combined with its validation through experimental EDX data, underlines its potential as a valuable tool for both research and industry. It can significantly aid in the development of more efficient recycling processes and provide better insights into battery degradation pathways during their first and second life use cases. These advantages make the proposed methodology a promising solution for advancing battery analysis, recycling, and reuse efforts.

This work was conducted within the framework of the HELENA project, funded by the German Federal Ministry of Education and Research (BMBF).

References:

[1] Kirkaldy, N., Samieian, M. A., Offer, G. J., Marinescu, M., Patel, Y., „Lithium-Ion Battery Degradation: Measuring Rapid Loss of Active Silicon in Silicon–Graphite Composite Electrodes,“ ACS Applied Energy Materials, vol. 5, no. 11, pp. 13367-13376, 2022. doi:10.1021/acsaem.2c02047.

[2] Mohtat, P., Lee, S., Sulzer, V., Siegel, J. B., Stefanopoulou, A. G., „Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates,“ Journal of The Electrochemical Society, vol. 167, no. 11, p. 110561, 2020. doi:10.1149/1945-7111/aba5d1.

[3] Schmitt, J., Schindler, M., Jossen, A., „Change in the half-cell open-circuit potential curves of silicon–graphite and nickel-rich lithium nickel manganese cobalt oxide during cycle aging,“ Journal of Power Sources, vol. 506, p. 230240, 2021. doi:10.1016/j.jpowsour.2021.230240.

[4] Schmitt, J., Rehm, M., Karger, A., Jossen, A., „Capacity and degradation mode estimation for lithium-ion batteries based on partial charging curves at different current rates,“ Journal of Energy Storage, vol. 59, p. 106517, 2023. doi:10.1016/j.est.2022.106517.

[5] Weng, A., Siegel, J. B., Stefanopoulou, A., „Differential voltage analysis for battery manufacturing process control,“ Frontiers in Energy Research, vol. 11, 2023. doi:10.3389/fenrg.2023.1087269.