Real-time parameter estimation of polymer electrolyte membrane fuel cell in absence of excitation
Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents a...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/394547 |
| Acceso en línea: | https://hdl.handle.net/2117/394547 https://dx.doi.org/10.1016/j.ijhydene.2023.08.041 |
| Access Level: | acceso abierto |
| Palabra clave: | Parameter estimation Real-time data processing Proton exchange membrane fuel cells Polymer electrolyte membrane fuel cell (PEMFC) Real-time Persistence of excitation Ohmic resistance Estimació de paràmetres Temps real (Informàtica) Piles de combustible de membrana d'intercanvi de protons Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents and compares three algorithms to estimate in real-time some critical PEMFC parameters in the voltage equation: the ohmic resistance, the charge transfer coefficient and the oxygen activity of a proton exchange fuel cell. The first algorithm is a standard gradient descent, while the other two are based on a set of pre-preprocessing dynamics. It is shown that, while the gradient descent requires the persistence of excitation condition, the addition of the pre-processing dynamics ensures reliable estimation under significantly weaker excitation assumptions. Moreover, it is shown that the pre-processing dynamics improves the transient behaviour and noise performance of the estimators. The results are validated through a set of numerical simulations and in an experimental prototype, where sensor noise and unmodelled disturbances are considered. |
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