4.7 Article

Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification

Journal

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 46, Issue 21, Pages 11878-11893

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2021.01.037

Keywords

Polymer electrolyte membrane fuel cell; Control oriented fuel cell model; Experimental parameterization; Grey-box estimation; Parametric sensitivity

Funding

  1. AVL List GmbH
  2. Austrian Research Promotion Agency (FFG)

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To mitigate degradation and prolong the lifetime of polymer electrolyte membrane fuel cells, advanced control strategies are crucial, requiring accurate and computationally efficient fuel cell models. Parameterizing a model efficiently to experimental data is challenging due to a large number of unknown parameters and complex optimization problems. A parameterization scheme based on multiple structured state space models is proposed in this work, enabling flexibility in model parameterization through local linear models.
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. (C) 2021 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC.

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