From the cell to the stack. A chronological walk through the techniques to manufacture the PEFCs core

Heras, A. De las; Vivas, F.J.; Segura, F.; Andújar, José Manuel

doi:10.1016/j.rser.2018.07.036

Cited by 34 publications

(22 citation statements)

References 99 publications

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“…A FC is a nonlinear complex system based on the serial connection of individual cells making up a stack, together with other systems necessary to its proper operation (the socalled balance of plant or BoP, which is governed by a control system responsible for the best performance of the FC [3]). Compared to other green technologies, for example wind or photovoltaic generation [4], FC can operate in any geographic location, without taking into account environmental constraints (availability of sun, wind, etc.).…”

Section: Introductionmentioning

confidence: 99%

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

et al. 2020

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Hydrogen economy is one of the recently opened alternatives in the field of non-polluting energy. Hydrogen fuel cells show high performance, high reliability in stationary applications and minimal environmental impact. To increase the efficiency of the hydrogen fuel cell it is very important to have a good model to predict its dynamic behavior. In addition, this model must be able to adapt iteratively to the changes that occur in its performance due to operating conditions and even to the degradation through its lifespan. This paper presents the application of an iterative fuzzy modeling methodology based on the extended Kalman filter applied to a real hydrogen fuel cell. Two algorithms based on the Kalman filter will be compared with the well-known backpropagation algorithm from three different initializations: by uniform partitioning, subtractive clustering and CMeans clustering. The used data have been collected during the actual 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 fuel cell, which can be very useful to design an efficient fuel cell control system.

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Section: Introductionmentioning

confidence: 99%

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

et al. 2020

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“…The combination of fuel cells, electrolyzers, and hydrogen storage systems have proven to be attractive alternatives as reserve systems and surplus storage in renewable facilities, where production depends on the availability of energy [13]. Fuel cells are built from individual cells, together with gas conditioning, cooling and fuel injection, comprise the balance of plant (BoP), which is governed by a control system [14,15]. The energy production of a fuel cell is only limited by the amount of hydrogen available, which eliminates the typical restrictions of solar or wind plants, but this, in turn, can be produced in electrolyzers from excess energy.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

et al. 2020

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This work deals with the prediction of variables for a hydrogen energy storage system integrated into a microgrid. Due to the fact that this kind of system has a nonlinear behaviour, the use of traditional techniques is not accurate enough to generate good models of the system under study. Then, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the power, the hydrogen level and the hydrogen system degradation. In this research, a hybrid intelligent model was created and validated over a dataset from a lab-size migrogrid. The achieved results show a better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption/generation with a mean absolute error of 0.63% with the test dataset respect to the maximum power of the system.

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“…A fuel cell is a complex system consisting of a series connection of individual cells (a stack), where the electric current is produced by an electrochemical reaction, combined with all other systems necessary for its operation, that is, filters and systems that condition the gases involved in the reaction ( 2 and 2 ), a cooling system, and, of course, a control system [3]. Compared to other clean technologies, such as wind or photovoltaic generation, fuel cells do not require a specific location to obtain higher performance.…”

Section: Introductionmentioning

confidence: 99%

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

et al. 2019

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A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.

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From the cell to the stack. A chronological walk through the techniques to manufacture the PEFCs core

Cited by 34 publications

References 99 publications

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

Hybrid Intelligent Modelling in Renewable Energy Sources-Based Microgrid. A Variable Estimation of the Hydrogen Subsystem Oriented to the Energy Management Strategy

Fuel Cell Output Current Prediction with a Hybrid Intelligent System

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