Revisiting parameter identification in electrochemical impedance spectroscopy: Weighted least squares and optimal experimental design

Abstract In this article several computational tools related to parameter identification and optimal experimental design (OED) in electrochemical impedance spectroscopy (EIS) are introduced. Weighted and iteratively reweighted least squares are revisited and are coupled to an optimization procedure, which aims at increasing the confidence on the estimated parameters and/or at shortening the experimental time without compromising the accuracy of the estimates. A sequential algorithm allowing real-time implementation of OED is also developed. A fuel cell electrode system model is used to test and validate the methods developed.

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