2024 AIChE Annual Meeting
Integrated Electrochemical-Acoustic Modeling for in-Silico Characterization of Lithium-Ion Batteries
Lithium-ion batteries are a key focus in the drive for energy transition due to their modularity and high energy density. Recent research efforts have been directed at studying the acoustic characterization of cells as a non-invasive probe for chemomechanical dynamics to complement electrochemical signals. However, a challenge in the progress of this novel characterization technique has been a disconnect between the two domains. Here, we address this in silico by coupling the Python Battery Mathematical Model (PyBaMM) with the acoustic k-Wave model. By using the electrochemical model as a feedstock for the mechanical one, we achieve continuity in the interpretation of acoustic features. This hybrid model is tuned and validated against experimental data from commercial-grade pouch cells. The framework developed in this study highlights the importance of integrated modeling solutions for battery acoustics for enhanced diagnostics of lithium-ion batteries.