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Adaptive online parameter estimation algorithm of PEM fuel cells

By Yashan Xing, Jing Na and Ramon Costa-Castelló

Abstract

Trabajo presentado en el 18th European Control Conference (ECC), celebrado en Nápoles (Italia), del 25 al 28 de junio de 2019Since most of fuel cell models are generally nonlinearly parameterized functions, existing modeling techniques rely on the optimization approaches and impose heavy computational costs. In this paper, an adaptive online parameter estimation approach for PEM fuel cells is developed in order to directly estimate unknown parameters. The general framework of this approach is that the electrochemical model is first reformulated using Taylor series expansion. Then, one recently proposed adaptive parameter estimation method is further tailored to estimate the unknown parameters. In this method, the adaptive law is directly driven by the parameter estimation errors without using any predictors or observers. Moreover, parameter estimation errors can be guaranteed to achieve exponential convergence. Besides, the online validation of regressor matrix invertibility are avoided such that computation costs can be effectively reduced. Finally, comparative simulation results demonstrate that the proposed approach can achieve better performance than least square algorithm for estimating unknown parameters of fuel cells.This work was partially funded by the Spanish national project MICAPEM (ref. DPI2015- 69286-C3-2-R, MINECO/FEDER) and the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). This work was partially supported by AGAUR of Generalitat de Catalunya through the Advanced Control Systems (SAC) group grant (2017 SGR 482) and Chinese Scholarship Council (CSC) under grant (201808390007). This work has been done with the partial support of ACCIO (Operational Program FEDER Catalunya 2014-2020) through the ´ REFER project (COMRDI15-1-0036-11

Topics: PEM Fuel Cell, Nonlinearly parameterized system, Online parameter estimation
Publisher: 'Institute of Electrical and Electronics Engineers (IEEE)'
Year: 2020
DOI identifier: 10.23919/ECC.2019.8795875
OAI identifier: oai:digital.csic.es:10261/206474
Provided by: Digital.CSIC
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