Structural Identifiability of a Pseudo-2D Li-ion Battery Electrochemical Model

TL;DR

This paper performs a structural identifiability analysis on a simplified pseudo-2D Li-ion battery model, establishing conditions for unique parameter estimation to improve internal state predictions.

Contribution

It introduces a structural identifiability analysis for a linearised pseudo-2D battery model, identifying 21 uniquely identifiable parameter combinations.

Findings

Model is parametrized by 21 identifiable parameters.

Parameter estimation problem is well-posed.

Results enable more accurate internal state predictions.

Abstract

Growing demand for fast charging and optimised battery designs is fuelling significant interest in electrochemical models of Li-ion batteries. However, estimating parameter values for these models remains a major challenge. In this paper, a structural identifiability analysis was applied to a pseudo-2D Li-ion electrochemical battery model that can be considered as a linearised and decoupled form of the benchmark Doyle-Fuller-Newman model. From an inspection of the impedance function, it was shown that this model is uniquely parametrised by 21 parameters, being combinations of the electrochemical parameters like the conductivities and diffusion coefficients. The well-posedness of the parameter estimation problem with these parameters was then established. This result could lead to more realistic predictions about the internal state of the battery by identifying the parameter set that can be uniquely identified from the data.