Identifiability of generalised Randles circuit models

TL;DR

This paper investigates whether parameters of generalized Randles circuit models, used in energy storage and biomedical applications, can be uniquely identified from data, establishing conditions for local and global identifiability and evaluating estimation accuracy.

Contribution

It provides a theoretical analysis of the structural identifiability of generalized Randles circuit models and discusses conditions for global identifiability, supported by extensive simulations.

Findings

Models are structurally locally identifiable.

Conditions for global identifiability are identified.

Estimation accuracy is validated through simulations.

Abstract

The Randles circuit (including a parallel resistor and capacitor in series with another resistor) and its generalised topology have widely been employed in electrochemical energy storage systems such as batteries, fuel cells and supercapacitors, also in biomedical engineering, for example, to model the electrode-tissue interface in electroencephalography and baroreceptor dynamics. This paper studies identifiability of generalised Randles circuit models, that is, whether the model parameters can be estimated uniquely from the input-output data. It is shown that generalised Randles circuit models are structurally locally identifiable. The condition that makes the model structure globally identifiable is then discussed. Finally, the estimation accuracy is evaluated through extensive simulations.