A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

José Agustín Aguilar, Damien Chanal, Didier Chamagne, Nadia Yousfi Steiner, Marie-Cécile Péra, Attila Husar and Juan Andrade-Cetto ()
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José Agustín Aguilar: Institut de Robòtica i Informàtica Industrial, Consejo Superior de Investigaciones Científicas-Universitat Politèctnica de Catalunya, Llorens Artigas 4-6, 08028 Barcelona, Spain
Damien Chanal: Institut FEMTO-ST, Université de Franche-Comté, UTBM CNRS, F-90000 Belfort, France
Didier Chamagne: Institut FEMTO-ST, Université de Franche-Comté, UTBM CNRS, F-90000 Belfort, France
Nadia Yousfi Steiner: Institut FEMTO-ST, Université de Franche-Comté, UTBM CNRS, F-90000 Belfort, France
Marie-Cécile Péra: Institut FEMTO-ST, Université de Franche-Comté, UTBM CNRS, F-90000 Belfort, France
Attila Husar: Institut de Robòtica i Informàtica Industrial, Consejo Superior de Investigaciones Científicas-Universitat Politèctnica de Catalunya, Llorens Artigas 4-6, 08028 Barcelona, Spain
Juan Andrade-Cetto: Institut de Robòtica i Informàtica Industrial, Consejo Superior de Investigaciones Científicas-Universitat Politèctnica de Catalunya, Llorens Artigas 4-6, 08028 Barcelona, Spain

Energies, 2024, vol. 17, issue 2, 1-20

Abstract: The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

Keywords: PEMFC; hybrid model; ESN; radial basis functions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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