An important aspect of lithium-ion batteries related to lifetime and aging is the change in state within the cells, which results from the expansion of the electrode materials and causes internal stress during operation. In this work, fiber optical sensors by means of Bragg gratings are utilized to determine the internal strain in the anode material. The collected data were employed to approximate aging-related changes in anode strain using a combination of established methods, such as the differential voltage and incremental capacity analysis. Moreover, additional methodologies are proposed and explored, substituting electrical data with optical strain measurements to quantify degradation effects linked to changes in strain. During the cycling of the cell, changes in the strain behavior have been observed and can be partially attributed to changes in the cell’s electrochemical composition. The methods suggested have proven effective in providing additional insights into the current state of the cells and tracking changes over time due to detected degradation effects.

This research investigates the use of a fiber optic sensor for detecting hydrogen gas during a thermal runaway of lithium-ion batteries (LIBs). Timely detection of thermal runaway in LIBs, particularly in storage and logistics, is crucial for effective safety management and preventing the escalation of incidents to adjacent cells. The sensors employed in this study utilize fiber Bragg grating (FBG) technology. The FBG sensors are coated with palladium nanoparticles, enabling the detection of hydrogen concentrations up to 5%. In abuse tests, the sensors successfully identified hydrogen emissions. Cross-sensitivity effects were observed during a secondary test and were thoroughly investigated. These interferences were found to be primarily caused by carbon monoxide (CO), a common byproduct of battery venting. While the presence of CO can interfere with hydrogen detection, both signals remain independently valuable as indicators of cell malfunction. This dual-response behavior enhances the robustness of fault detection under real-world battery failure scenarios.