Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers: Model based optimal design of experiments supported by parameter sensitivity analysis. (14th April 2021)

• Record Type: Journal Article
• Authors: Kravos, Andraž; Ritzberger, Daniel; Hametner, Christoph; Jakubek, Stefan; Katrašnik, Tomaž
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 26(2021)
• Page Start: 13832
• Page End: 13844
• Publication Date: 2021-04-14
• Subjects: PEM fuel cells -- Electrochemical PEM FC performance model -- Model-based design of experiments -- Reduced dimensionality models -- Parameter sensitivity -- Virtual observers; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2020.10.146
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Determination of the optimal design of experiments that enables efficient parametrisation of fuel cell (FC) model with a minimum parametrisation data-set is one of the key prerequisites for minimizing costs and effort of the parametrisation procedure. To efficiently tackle this challenge, the paper present an innovative methodology based on the electrochemical FC model, parameter sensitivity analysis and application of D-optimal design plan. Relying on this consistent methodological basis the paper answers fundamental questions: a) on a minimum required data-set to optimally parametrise the FC model and b) on the impact of reduced space of operational points on identifiability of individual calibration parameters. Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets. In addition, it was shown that increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model. Highlights: Analysis is based on a thermodynamically consistent electrochemical fuel cell model. Optimal set of fuel cell model parameters is determined by sensitivity analysis. D-optimal criterion is used to determine the optimal design of experiments. Enhanced parameter information results in up to few orders of magnitude lower RSE. It is shown that variation of inlet pressure significantly increases information.