Li-ion batteries are more prone to fires than other battery technologies due to their inherent higher energy density as well as the flammability of especially electrode materials, separator, and electrolyte solvents. Ageing and degradation can contribute to considerably decrease the safety of a Li-ion battery, but the exact impact from ageing on safety of battery cells and modules is still not well understood today.

Incremental capacity analysis (ICA, dQ/dV) has emerged as a very powerful diagnostic technique for Li-ion batteries. We recently used ICA to identify different degradation mechanisms in commercial Li-ion cells through classification and tracking of selected dQ/dV features [1]. This allowed us to identify safety critical ageing at an early stage. ICA requires constant charge and discharge at slow currents (C-rate < C/10) [2, 3]. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to battery systems without access to high precision battery pack testers. As opposed to constant-current (CC) measurements across the full SoC window, (pseudo-) OCV values might be considerably easier to obtain during actual battery operation within an application. In fact, the OCV curve can be obtained from any sequence of discharge or charge current or power pulse with the necessary rest period to allow the cell to reach a pseudo-OCV after each pulse. This will allow for the establishment of a pseudo-OCV curve without significant operational interruption[4, 5]. In this work we will revisit applying ICA on the Open-Circuit-Voltage (OCV) curve in the capacity space [4, 5]. By pulsing through the entire state-of-charge window an OCV vs capacity curve can be obtained with sufficient accuracy to perform ICA. A direct comparison of conventional constant current ICA (cc-ICA) and high-resolution-OCV ICA (OCV-ICA) is presented. A strong correlation between ageing patterns is observed providing a first proof-of-concept for the method [6]. References: 1. Spitthoff, L., et al., Incremental Capacity Analysis (dQ/dV) as a Tool for Analysing the Effect of Ambient Temperature And Mechanical Clamping on Degradation. Journal of Electroanalytical Chemistry, 2023. 2. Dubarry, M. and D. Anseán, Best practices for incremental capacity analysis. Frontiers in Energy Research, 2022. 10: p. 18. 3. Dubarry, M., et al., Incremental capacity analysis and close-to-equilibrium OCV measurements to quantify capacity fade in commercial rechargeable lithium batteries. Electrochemical and Solid State Letters, 2006. 9(10): p. A454-A457. 4. Petzl, M. and M.A. Danzer, Advancements in OCV Measurement and Analysis for Lithium-Ion Batteries. Ieee Transactions on Energy Conversion, 2013. 28(3): p. 675-681. 5. Goldammer, E. and J. Kowal. Investigation of degradation mechanisms in lithium-ion batteries by incremental open-circuit-voltage characterization and impedance spectra. in 17th IEEE Vehicle Power and Propulsion Conference (VPPC). 2020. Electr Network: IEEE. 6. Wind, J. and P.J.S. Vie, Revisiting Pulse-Based OCV Incremental Capacity Analysis for Diagnostics of Li-Ion Batteries. Batteries-Basel, 2024. 10(8): p. 12. Figure: Illustration of the concept of OCV-ICA applied to operational data. OCV data is extracted from (a) and the OCV is reconstructed from relaxation data, while ICA is further calculated from the OCV curve (b). Changes in OCV-ICA due to ageing are compared in (c). [6]