Adaptive online parameter estimation algorithm of PEM fuel cells
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
Since 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.
CitationXing, Y.; Jing, N.; Costa-Castelló, R. Adaptive online parameter estimation algorithm of PEM fuel cells. A: European Control Conference. "Proceedings of the 18th European Control Conference (ECC): Naples, Italy: June 25-28, 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 441-446.