RT Journal Article
T1 Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter
A1 Barragán Piña, Antonio Javier
A1 Enrique Gómez, Juan Manuel
A1 Segura Manzano, Francisca
A1 Andújar Márquez, José Manuel
AB Hydrogen economy is one of the recently opened alternatives in the field of non-pollutingenergy. Hydrogen fuel cells show high performance, high reliability in stationary applications and minimalenvironmental impact. To increase the efficiency of the hydrogen fuel cell it is very important to have agood model to predict its dynamic behavior. In addition, this model must be able to adapt iteratively tothe changes that occur in its performance due to operating conditions and even to the degradation throughits lifespan. This paper presents the application of an iterative fuzzy modeling methodology based on theextended Kalman filter applied to a real hydrogen fuel cell. Two algorithms based on the Kalman filter willbe compared with the well-known backpropagation algorithm from three different initializations: by uniformpartitioning, subtractive clustering and CMeans clustering. The used data have been collected during theactual operation of a real 3.4 kW proton exchange membrane fuel cell. As the article experimentally shows,the Takagi-Sugeno type fuzzy model allows to create a very accurate nonlinear dynamic model of the fuelcell, which can be very useful to design an efficient fuel cell control system
PB Institute of Electrical and Electronics Engineers
SN 2169-3536 (electrónico)
YR 2020
FD 2020-10
LK https://hdl.handle.net/10272/23632
UL https://hdl.handle.net/10272/23632
LA eng
NO Barragan, A. J., Enrique, J. M., Segura, F., & Andujar, J. M. (2020). Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter. In IEEE Access (Vol. 8, pp. 180280–180294). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/access.2020.3013690
NO This work was supported by the Spanish Ministry of Economy Industry and Competitiveness through the H2SMART- µGRID Project under Grant DPI2017-85540-R.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026