Electrochemical impedance spectroscopy (EIS) provides valuable insights into the physical processes within batteries – but how can these measurements directly inform physics-based models? In this webinar, we present recent work showing how impedance data can be used to extract grouped parameters for physics-based models such as the Doyle–Fuller–Newman (DFN) model or the reduced-order single-particle model with electrolyte (SPMe). We will introduce PyBaMM (Python Battery Mathematical Modelling), an open-source framework for flexible and efficient battery simulation, and show how our extension, PyBaMM-EIS, enables fast numerical impedance computation for any implemented model at any operating point. We also demonstrate how PyBOP, another open-source tool, performs automated parameter fitting of models using measured impedance data across multiple states of charge. Battery modelling is challenging, and obtaining accurate fits can be difficult.…