Publication

Real-time adaptive parameter estimation for a polymer electrolyte membrane fuel cell

Journal Article (2019)

Journal

IEEE Transactions on Industrial Informatics

Pages

6048-6057

Volume

15

Number

11

Doc link

http://dx.doi.org/10.1109/TII.2019.2915569

File

Download the digital copy of the doc pdf document

Abstract

In this paper we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the well-recognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenarios.

Categories

adaptive control, control nonlinearities.

Author keywords

Adaptive parameter estimation, nonlinearly parametric system, polymer electrolyte membrane fuel cell (PEMFC), time-varying parameters

Scientific reference

Y. Xing, J. Na and R. Costa Castelló. Real-time adaptive parameter estimation for a polymer electrolyte membrane fuel cell. IEEE Transactions on Industrial Informatics, 15(11): 6048-6057, 2019.