Control-oriented computational fuel cell dynamics modeling – Model order reduction vs. computational speed

Jian Zhao, Xianguo Li, Chris Shum and John McPhee

Energy, 2023, vol. 266, issue C

Abstract: Many 1D computational fuel cell dynamics models are established to predict the performance of proton exchange membrane (PEM) fuel cells for control purposes. The 1D models are of high fidelity over a wide range of operating conditions in comparison with empirical and analytical models and less dependent on a large quantity of experimental data in comparison with data-driven models. However, the relation between the various simplified 1D models and their computing efficiency has not been fully understood. Therefore, the present study investigates the trade-off relation between the model order reduction and computational speed of control-oriented 1D models. A full-order model, considering all essential physical phenomena, is validated against experimental data. Seven reduced-order models, depending on whether convection, liquid water, and thermal effects are simulated, are compared with the full-order model in terms of accuracy and computational speed under the same boundary and initial conditions. The results indicate that the two-phase flow has a more significant impact on the cell performance than non-isothermal and convection effects at high relative humidity and current density conditions. The computing speed of the reduced-order model, in which the convection, liquid water, and temperature variation are omitted, can be 140 times faster than the full-order model.

Keywords: Control-oriented modeling; Model order reduction; Fuel cell; Transport phenomena; Electrochemical kinetics (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033746

DOI: 10.1016/j.energy.2022.126488

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