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 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.
This work has been supported by the Spanish Ministry of Universities funded by the European Union - NextGenerationEU (2022UPC-MSC-93823). This work is part of the Project MAFALDA (PID2021- 126001OB-C31) funded by MCIN/ AEI /10.13039/501100011033 and by "ERDF A way of making Europe" This work is part of the project MASHED (TED2021-129927BI00), funded by MCIN/ AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR. This research has been developed within the CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+) as part of the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094.
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