Recent Trends in the Mathematical Modelling of Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries
Authors: Concepcion Anthony , Telmasre Tushar , Subramanian Venkat R. 
Published: August 2024

Electrochemical Impedance Spectroscopy (EIS) has emerged as a great alternative tool to characterize electrochemical energy storage systems such as batteries[1, 2]. A commercial Li-ion battery is essentially an electrochemical system having a multitude of physical and chemical processes occurring simultaneously while in operation. Each of these processes is governed by thermodynamic and kinetic principles and possesses a characteristic time scale. Thus, based on the frequency response, we can distinguish between individual mechanisms operating within the battery in a non-destructive manner via EIS [1].
Numerical modeling is a powerful tool to understand the battery system from a theoretical perspective[3]. Development of high-fidelity, multiscale, and multicomponent models can be a complex and computationally expensive endeavor. Therefore, simple models, with generalization about the physics of the problem, are sought initially upon which more complex models are built.
Traditional EIS simulation approaches include Equivalent Circuit Models (ECMs), which aim to represent the battery as an electrical circuit. This approach suffers from its empirical nature and lack of physical interpretability[2]. Another approach is to work with physics-based models. Electrochemical Impedance Spectroscopy, being a frequency domain technique, needs a special treatment to be done on the time domain Partial Differential Equations (PDEs) to be solved in a steady state. This process involves linearizing the model equations and applying the Laplace transform. Physics-based models have a better fidelity but suffer from high computational costs.[4] Therefore, fast, reduced-order physics-based models have been developed by researchers that aim to maintain the physical consistency of the model at the same time reducing the computational cost.[5] Recent years have also seen a surge in new machine learning-based techniques to replicate the impedance response of the batteries.[6]
The present work aims to summarize the various techniques of EIS modeling and discuss their relative advantages and shortcomings in a comprehensive manner.

References:

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