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Comparative Analysis of Two Online Identification Algorithms in a Fuel Cell System
Author(s) -
Kandidayeni M.,
Macias A.,
Amamou A. A.,
Boulon L.,
Kelouwani S.
Publication year - 2018
Publication title -
fuel cells
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.485
H-Index - 69
eISSN - 1615-6854
pISSN - 1615-6846
DOI - 10.1002/fuce.201800025
Subject(s) - computer science , robustness (evolution) , algorithm , fuel cells , stack (abstract data type) , identification (biology) , online model , energy management , energy (signal processing) , mathematics , engineering , botany , chemical engineering , biology , biochemistry , chemistry , statistics , gene , programming language
Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well‐known recursive algorithms are compared for online estimation of a multi‐input semi‐empirical FC model parameters. In this respect, firstly, a semi‐empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV.