Read the research.

The method rests on published, peer-reviewed work. These papers show what acoustic emission reveals as a cell forms, ages and fails, and how the signal is captured and classified. Every one is open access.

Formation

  1. Utilising acoustic techniques to improve understanding of the formation process in sodium-ion batteries

    Uses acoustic techniques to follow what happens inside sodium-ion cells during the formation process.

    Fordham, A., Wee, D., Jackowska, R., Jia, T., Zheng, Y. and Kendrick, E. EES Batteries, 2026.

Ageing and degradation

  1. Deconvoluting silicon-graphite electrode degradation using acoustic emission and wavelet analysis

    Separates the acoustic signatures of different degradation processes in silicon-graphite electrodes.

    Noll, L., Rueß, V. and Schröder, D. Journal of Power Sources 668, 2026.

  2. Acoustic emissions testing as a complementary tool to understand chemical and electrochemical changes in battery electrodes

    Shows how acoustic-emission testing adds to conventional electrochemical measurements when studying changes in electrodes.

    Noll, L., Mrowetz, J., Kretschmer, K. and Schröder, D. Journal of Power Sources 629, 2025.

  3. Correlative non-destructive techniques to investigate aging and orientation effects in automotive Li-ion pouch cells

    Combines non-destructive methods, including acoustics, to study how ageing and cell orientation affect automotive pouch cells.

    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. Joule 7, 2622–2652, 2023.

Safety and failure

  1. Safety and performance implications of lithium plating induced by sub-zero temperature cycling of lithium-ion batteries

    Examines what cycling below zero does to a cell and how the resulting lithium plating affects its safety and performance.

    Kirchner-Burles, C., Fordham, A., Reid, H. T., Johnson, M., Buckwell, M., Iacoviello, F., Coke, K., Jervis, R., Hinds, G., Shearing, P. R. et al. Journal of Power Sources 660, 2025.

  2. Investigating the performance and safety of Li-ion cylindrical cells using acoustic emission and machine learning analysis

    Applies machine learning to acoustic-emission signals from commercial cylindrical cells in performance and safety testing.

    Fordham, A., Joo, S.-B., Owen, R. E., Galiounas, E., Buckwell, M., Brett, D. J. L., Shearing, P. R., Jervis, R. and Robinson, J. B. Journal of The Electrochemical Society 171(7), 070521, 2024.

Method and machine learning

  1. Listening to batteries: using feature extraction and machine learning to identify and predict gas evolution and particle cracking with acoustic emission analysis

    Extracts features from acoustic-emission signals and uses machine learning to tell gas evolution from particle cracking.

    Noll, L., Rueß, V., Göhrmann, M. and Schröder, D. Journal of Power Sources 661, 2026.

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