Lógica borrosa para la estimación de estados críticos de una pila de combustible PEM
Abstract
RESUMEN
La determinación en tiempo real de los estados críticos de operación de la pila de combustible de membrana intercambio protónico (siglas en ingles, PEM) es uno de los principales retos para los sistemas de control de pilas de combustible PEM. En este trabajo, se presenta el desarrollo e implementación de un método no invasivo de bajo coste basado en técnicas de decisión borrosa que permite estimar los estados críticos de operación de la pila de combustible PEM. La estimación se realiza mediante perturbaciones al estado de operación de la pila y el análisis posterior de la evolución temporal del voltaje generado por la pila. La implementación de esta técnica de estimulación-percepción de estado de la pila de combustible para la detección de estados críticos constituye una novedad y un paso hacia el control autónomo en óptimas condiciones de la operación de las pilas de combustible PEM.
Palabras clave: Caracterización de pilas de combustible PEM, estado de inundación y deshidratación de la membrana polimérica, árbol de decisión borroso, control, lógica difusa.
ABSTRACT
The real time determination of the critical states of operation of the fuel cell proton exchange membrane (acronym in English, PEM) is one of the main challenges for the control systems of PEM fuel cells. In this paper, the development and implementation of a non-invasive low cost method based on fuzzy decision techniques to estimate the critical states of operation of the PEM fuel cell is presented. The estimation is performed by perturbations of the state of operation of PEM fuel cell and the subsequent analysis of the temporal evolution of the voltage generated by the cell. The implementation of this stimulation-perceived technique of the state of fuel cell for the detection of critical states is a novelty and a step towards autonomous control in optimal operation of PEM fuel cells.
Keywords: PEMFC characterization, membrane flooding and dehydratation state, fuzzy decision tree, control, fuzzy logic.
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References
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