Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification. (23rd March 2021) - Ritzberger, Daniel; Höflinger, Johannes; Du, Zhang Peng; Hametner, Christoph; Jakubek, Stefan
Abstract: In order to mitigate the degradation and prolong the lifetime of polymer electrolyte membrane fuel cells, advanced, model-based control strategies are becoming indispensable. Thereby, the availability of accurate yet computationally efficient fuel cell models is of crucial importance. Associated with this is the need to efficiently parameterize a given model to a concise and cost-effective experimental data set. A challenging task due to the large number of unknown parameters and the resulting complex optimization problem. In this work, a parameterization scheme based on the simultaneous estimation of multiple structured state space models, obtained by analytic linearization of a candidate fuel cell stack model, is proposed. These local linear models have the advantage of high computational efficiency, regaining the desired flexibility required for the typically iterative task of model parameterization. Due to the analytic derivation of the local linear models, the relation to the original parameters of the non-linear model is retained. Furthermore, the local linear models enable a straight-forward parameter significance and identifiability analysis with respect to experimental data. The proposed method is demonstrated using experimental data from a 30 kW commercial polymer electrolyte membrane fuel cell stack. Graphical abstract: Image 1 Highlights: Data-driven parameterization of a 0D dynamic PEMFC stack model. Computational efficient parameterization methodology. Simultaneous identification of analytic local linear models. Parametric output sensitivity-based identifiability analysis. Experimental validation on a commercial 30 kW PEMFC stack.