In our work, we present PyBaMM, an open-source Python library for simulating the behavior of mainly lithium-ion batteries, as well as some others. Using it, we perform Electrochemical Impedance Spectroscopy (EIS) on the DFN model with a differential
formulation of surface diffusion, and we show that the output data is of high quality. From the raw data, we aim to obtain Nyquist plots using three different data processing methods: peak detection, sinusoidal approximation, and Fast Fourier Transform (FFT). The latter two prove to be more reliable, and the error between them is negligible. We then compared the peak detection and FFT methods with an external package, pybamm-eis, which is designed for direct EIS analysis with the PyBaMM library. It turns out that the peak detection method may deviate, while the latter two are comparable.
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