Fuel Cell Output Current Prediction with a Hybrid Intelligent System

Casteleiro‐Roca, José‐Luis; Barragán, Antonio Javier; Segura, F.; Calvo-Rolle, José Luís; Andújar, José Manuel

doi:10.1155/2019/6317270

Cited by 33 publications

(13 citation statements)

References 55 publications

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“…The architecture of the microgrid is characterized by the use of a mixed topology, which uses DC and AC buses to ensure the interconnection between equipment and the main electrical grid [42]. All of the generation and consumption subsystems are connected to the internal DC bus, supported by the supercapacitor and battery bank, allowing a two-way energy flow between the main electrical grid of the campus and the microgrid.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…The internal reactions can be divided into three different types, depending on the part of the cell: the anode, the cathode, and the global result. Equation (1) shows these three chemical reactions [31,32]. The fuel cell type used in this research is a Proton Exchange Membrane Fuel Cell (PEMFC), one of the most efficient technologies.…”

Section: Fuel Cellmentioning

confidence: 99%

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

et al. 2019

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The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73 with the validation dataset.

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“…The Polynomial regression algorithm is an old technique used to achieve a linear regression function. It is based on some basis functions that are summed [77][78][79][80][81]. The number of inputs and the degree of the used polynomial affect the number of these basis functions.…”

Section: Polynomial Regressionmentioning

confidence: 99%

Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand

et al. 2019

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Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.

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Fuel Cell Output Current Prediction with a Hybrid Intelligent System

Cited by 33 publications

References 55 publications

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

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

Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System

Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand

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