An Algorithmic Framework for Full-order Physics‑based Simulations of Electrochemical Impedance Spectroscopy

Simulated Electrochemical Impedance Spectroscopy (sEIS) is a powerful technique for non-invasive analysis of lithium-ion batteries (LiBs). It virtually replicates experimental EIS by applying small-signal current perturbations to a physics-based LiB model to observe the resulting impedance response, enabling applications like model parameterisation and degradation characterisation. To advance this area of sEIS research, this work proposes a novel solver to simulate a full-order physics-based model called the Electrochemical-Ageing-Capacitance (EAC) model. The contributions of this work are threefold. First, the equations of the EAC model are transformed from a set of coupled partial-differential-equations (PDEs) and ordinary-differential-equations (ODEs) into a coupled ODE-only system. Second, a novel ‘ODE+iterative’ solver framework is proposed to accurately and efficiently compute the EAC model equations. To benchmark performance, the solver is compared with state-of-the-art solvers in both MATLAB and PyBaMM. It demonstrates <1% prediction error for most EAC model variables. When computing sEIS impedance spectra, the solver also achieves a 4x improvement in solving performance compared to MATLAB, and competitive performance compared to PyBaMM. Finally, we present a novel demonstration of using sEIS to quantitatively characterise degradation in the EAC model. The solver is provided open-source, offering researchers a validated and efficient tool for high-fidelity sEIS simulations.