Impedance model for diagnosis of water management in fuel cells using artificial neural networks methodology

Laribi, Slimane; Mammar, Khaled; Messaoud, Hassani; Sahli, Youcef

doi:10.1016/j.ijhydene.2016.07.099

Cited by 38 publications

(18 citation statements)

References 20 publications

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“…The technic chosen for the modeling of solar cells is the technic of ANN method, which consists of three steps; the choice of neuronal structure, learning and validation. [13,22]. Table 2.…”

Section: The Ann Approachmentioning

confidence: 99%

“…However, these conditions are not always obvious, occurring seldom outside, because they are mainly carried out under conditions of the laboratory by using a solar simulator matirials. Consequently, to carry out a characterization appropriate to the behavior of electric modules regular minutes (obtaining curves I-V and P-V), recently, several authors [5][6] are used the artificial intelligence technics such as the fuzzy logic [5][6][7] and the artificial neuron networks (ANN) [2,[6][7][8][9][10][11][12][13] to modeling OPV cells. This approach is logical if one were to consider the dependence of the solar cell to any variations conditions of the environment [8].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Ghaitaoui¹,

Benatiallah²,

Khachab³

et al. 2019

EJEE

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In this paper, the characterization and modeling results of the electrical parameters of the tendem organic photovoltaic cells (infinityPV) are presented. The electrical performances of this organic cell's module (I-V and P-V) are characterized and analyzed in the weather condition of southwest Algeria (Adrar site). A program in MATLAB language is developed locally for modeling the organic cell, using artificial intelligence. The multilayer feed forward perception type ANN is used for extraction of the organic photovoltaic model performances, and the back-propagation algorithm is used for the program training. The ANN obtained results by the simulation showed a good conformity with the results obtained experimentally in the saharan climate (MAPE:0.68%, MBE:4.5018e-09 A and RMSE:1.8062e-04 A).

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Section: The Ann Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Ghaitaoui¹,

Benatiallah²,

Khachab³

et al. 2019

EJEE

Self Cite

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“…The novelty of this method relies on a simplified PEMFC model that exhibits excellent performance in terms of set‐point tracking, interference suppression, and robustness against parameter uncertainty and measurement noise. Laribi et al 17 used artificial neural network model to develop a fuel cell water management predictive diagnosis model, which has high sensitivity to membrane dryness and flooding, flexible and accurate response and fast. Zhao et al 18 used the orthogonal test method to conduct an experimental study on the dynamic performance and stability characteristics of the fuel cell system with dual exhaust gas recirculation.…”

Section: Introductionmentioning

confidence: 99%

Simulation study of proton exchange membrane fuel cell cross‐convection self‐humidifying flow channel

Tong

Yuan

et al. 2020

Int J Energy Res

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Summary The proton conducting membrane is the core component of the fuel cell. It needs water to maintain conductivity. Excessive water content inside the fuel cell will block the membrane surface and reduce the output power of the fuel cell. On the other hand, if the water content is too low, the internal resistance of the fuel cell will increase, which will reduce the performance and service life of the fuel cell. Considering the above problems, it is necessary to humidify the air and hydrogen gas before entering the fuel cell, but humidification is to prevent the membrane at the gas inlet from becoming dry. Although proton exchange membrane fuel cell (PEMFC) generates enough water, most of the gas is not completely saturated. This paper designs a self‐humidifying channel to redistribute the distribution of humid gas between different channels, and used FLUENT to simulate the heat and mass transfer, electrical conduction in the fuel cell. The effect of the self‐humidifying flow channel location on the PEMFC water and heat distribution is analyzed and evaluated.

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“…Compared to the other types of renewable energy technology, fuel cell has a stable power and a high-efficiency compared to the internal combustion engines and an independence from fossil resource use. PEM is a fuel cell type that works in the low temperature (70-120°C), it is characterized by a high efficiency and one of promising technologies in future [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The used fuel in this model is the pure hydrogen, although other gas compositions can be used in this process. [6].…”

Section: Introductionmentioning

confidence: 99%

Analyze of Impedance for Water Management in Proton Exchange Membrane Fue Fells Using Neural Networks Methodology

Laribi

Mammar

Arama

et al. 2019

AJRESD

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The objective of this work is to define and to implement a simple method to assess the impacts of relative humidity and operating time on the fuel cell impedance. The method is based on the physical model of Randles with CPE and a mathematical tool for identifying various parameters based on the least squares’ method. The objective of the theoretical model development is the model implementation of the control system and water management of predictive diagnostics. Artificial neural networks are used to create the optimum impedance model. The model is applied for the identification of all resistors (internal resistors measured at high frequency, biasing resistors measured at high frequency) which are characterized by a high sensitivity for both cases, the flooding or drying of the cell heart (membrane and electrodes). This model is able to easily generate Nyquist diagram for any condition of relative humidity and operating time, it helped define the stack hydration status. Based on the obtained results, the model demonstrated a best flexible response, accurate and fast. The developed model can be integrated into a water management control system in PEM fuel cells.

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Impedance model for diagnosis of water management in fuel cells using artificial neural networks methodology

Cited by 38 publications

References 20 publications

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Simulation study of proton exchange membrane fuel cell cross‐convection self‐humidifying flow channel

Analyze of Impedance for Water Management in Proton Exchange Membrane Fue Fells Using Neural Networks Methodology

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