Listening to Batteries: Using Acoustic Diagnostic Techniques for Electrochemical Characterisation to Improve Battery Performance and Safety

Understanding the degradation pathways and failure mechanisms of Li-ion batteries (LIBs) is crucial due to the rare but potentially significant chemical and fire hazards associated with their failure. Recent regulations mandate that Electric Vehicles (EVs) provide at least a 5-minute advance warning before thermal runaway, driving the development of advanced diagnostic techniques.[1] Acoustic techniques, including Acoustic Emission (AE) and Ultrasonic Testing (UT), have emerged as promising operando monitoring tools due to their cost-effectiveness, and non-invasive nature.[2–5] These methods enable real-time diagnostics by detecting physical and electrochemical changes within cells, such as gas formation, volume expansion, cracking, and delamination. This makes the techniques ideal for determining the State of Health (SoH) and State of Stability (SoS) while pinpointing degradation and failure pathways. This study presents the first application of AE and UT as a combined diagnostic tool for SoH/SoS monitoring and early failure detection, with the potential for integration into Battery Management Systems (BMS) as an early-warning notification. To validate these acoustic techniques, AE and UT were integrated with high-speed synchrotron X-ray radiography to correlate acoustic signals with dynamic cell changes during operation.[6] This approach confirmed the physical origins of distinct acoustic waveforms linked to gassing, cracking, and structural degradation (Figure 1). These combined acoustic and synchrotron radiography experiments were conducted on cells across a range of State of Charge (SoC) and SoH, including pristine cells, cells aged at room temperature, and those cycled at sub-zero temperatures, demonstrating the robustness of these diagnostic tools under diverse conditions. Bespoke classification algorithms, developed and trained on three years of degradation and battery failure data from various cell types, chemistries, formats, and capacities, were employed for the real-time classification of acoustic signals.[7] The findings demonstrate that AE and UT techniques can precisely identify degradation mechanisms and provide early alerts for potential failures based on distinctive waveforms. Observations from synchrotron radiography confirmed the physical origins of these signals, thereby validating the effectiveness of acoustic techniques as tools for SoH/SoC determination. By integrating acoustic diagnostics into BMS, this research will advance the safety standards of LIBs and mitigate against potential hazards across automotive, stationary storage, and manufacturing applications. This work highlights the key role of combined acoustic methods as advanced characterisation and diagnostic techniques in addressing the performance and safety requirements of various battery systems. References: [1]. Stephan Rindfleish (2022). Making EV’s safer. e-motec. https://www.e-motec.net/making-evs-safer#:~:text=One of them requires that,and North America in 2023. [2]. Fordham, A., Milojevic, Z., Giles, E., Du, W., Owen, R.E., Michalik, S., Chater, P.A., Das, P.K., Attidekou, P.S., Lambert, S.M., et al. (2023). Correlative non-destructive techniques to investigate aging and orientation effects in automotive Li-ion pouch cells. Joule 7, 2622–2652. 10.1016/j.joule.2023.10.011. [3]. Wang, Z., Lu, K., Chen, X., Zhen, D., Gu, F., and Ball, A.D. (2022). Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method. 1–3. [4]. Robinson, J.B., Owen, R.E., Kok, M.D.R., Maier, M., Majasan, J., Braglia, M., Stocker, R., Amietszajew, T., Roberts, A.J., Bhagat, R., et al. (2020). Identifying Defects in Li-Ion Cells Using Ultrasound Acoustic Measurements. J. Electrochem. Soc. 167, 120530. 10.1149/1945-7111/abb174. [5]. Majasan, J.O., Robinson, J.B., Owen, R.E., Maier, M., Radhakrishnan, A.N.P., Pham, M., Tranter, T.G., Zhang, Y., Shearing, P.R., and Brett, D.J.L. (2021). Recent advances in acoustic diagnostics for electrochemical power systems. JPhys Energy 3. 10.1088/2515-7655/abfb4a. [6]. Pham, M.T.M., Darst, J.J., Finegan, D.P., Robinson, J.B., Heenan, T.M.M., Kok, M.D.R., Iacoviello, F., Owen, R., Walker, W.Q., Magdysyuk, O. V., et al. (2020). Correlative acoustic time-of-flight spectroscopy and X-ray imaging to investigate gas-induced delamination in lithium-ion pouch cells during thermal runaway. J. Power Sources 470. 10.1016/j.jpowsour.2020.228039. [7]. Fordham, A., Joo, S., Owen, R.E., Galiounas, E., Buckwell, M., Brett, D.J.L., Shearing, P.R., and Jervis, R. (2024). Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis Investigating the Performance and Safety of Li-Ion Cylindrical Cells Using Acoustic Emission and Machine Learning Analysis. 10.1149/1945-7111/ad59c9. Figure 1