Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

Khan, Saad Saleem; Shareef, Hussain; Bouhaddioui, Chafik; Errouissi, Rachid

doi:10.1002/er.4927

Cited by 11 publications

(29 citation statements)

References 86 publications

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“…The model‐driven methods focus on the performance degradation mechanism analysis. In many model‐driven literatures, the empirical or semi‐empirical models are employed to achieve the performance prediction of PEMFC 4‐8 . The model‐driven methods require a deep understanding of physicochemical phenomena taking place in fuel cell and may vary with different types of fuel cells, which makes these model‐driven methods difficult to be applied in other types of fuel cells.…”

Section: Introductionmentioning

confidence: 99%

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Hou

Tao

et al. 2021

Int J Energy Res

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Performance prediction of proton-exchange membrane fuel cell (PEMFC) under dynamic conditions, especially for vehicle applications, is increasingly become the focus of attention. This article proposes a performance prediction method of PEMFC using long short-term memory (LSTM) recurrent neural network (RNN). In this article, polarization curve (current-voltage curve) and voltage degradation curve (current-time curve) are adopted as the main performance indexes of PEMFC. Both polarization curve prediction and performance degradation prediction of PEMFC can be effectively implemented based on the LSTM method. To investigate the voltage losses law of experimental and predicted results, the paper introduces an empirical equation of polarization curve. The perfect match between the experimental and predicted polarization losses of PEMFC can further validate the prediction performance of LSTM method. The proposed prediction method is validated by the PEMFC polarization curve data obtained from the designed aging experiment of a 4 kW stack operated under dynamic loading cycling situation during 600 hours. Then, LSTM network is compared with traditional RNN and back-propagation neural network (BPNN) to prove its superiority. The minimum values of rootmean-square error (RMSE) and the mean absolute percentage error (MAPE) of LSTM network with different training data are 0.0088 and 0.0101, respectively. All the coefficient of determination (R 2 ) of LSTM model with different training data is over 0.95, which is close to 1.0. The prediction accuracy of LSTM network is higher than that of two other networks. The result indicates that LSTM network outperforms two other networks in PEMFC performance prediction.Hence, the prediction method based on LSTM network is very suitable for PEMFC performance prediction.

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Section: Introductionmentioning

confidence: 99%

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Hou

Tao

et al. 2021

Int J Energy Res

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“…Recently Saad Saleem Khan et al . [17] proposed a developed model based on the quantum lightning search algorithm (QLSA). In this study, PEMFC was opening under various operating condition such as temperature and humidity.…”

Section: Introductionmentioning

confidence: 99%

Acoustical Characteristics of Proton Exchange Membrane Fuel Cells

2021

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In order to optimise the performance of Proton Exchange Membrane Fuel Cells (PEMFCs) and to maximise their life cycle, water content has to be properly controlled. This paper presents a model-based method to determine the effect of load on PEMFC water content in PEMFCs using Acoustic Emission (AE) measurements. The developed model was implemented in COMSOL and verified using a single proton exchange membrane fuel cell (FC) operated under various loads. Acoustical events originating from water bubble formation have been identified and assessed using statistical parameters determined in the time and frequency domains. The feasibility of using AE techniques to detect and monitor the impact of a cell's load variation on water content is assessed. As the model results were in good agreement with experimental data, it was concluded that an AE based method could serve as an effective monitoring and control tool of water content in PEMFCs. Statically, the root mean square for acoustic emission activity has a significant relation with load characteristics and can, potentially, be a candidate for determining the AE behavior in a PEMFC.

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“…In [18] the effect of ambient pressure and temperature change on PEMFC voltage is directly modelled by using statistical analysis. However the most recent work on PEMFC modeling by considering ambient conditions is done in [19] which is an extension of the model mentioned in [20], where ambient air relative humidity and ambient temperature change is directly incorporated in the model parameters. Unlike the previous models mentioned the membrane water content is not constant in [19] which makes this model suitable for drying and flooding fault diagnosis, the effect of internal currents which was first investigated in [16] has also been considered.…”

Section: Introductionmentioning

confidence: 99%

“…The model is tested for NEXA 1200 W PEMFC system and Horizon 300 W PEMFC system. Additional advantage of using the model in [19] is that it uses membrane water content as variable which is missing in all other modern models mentioned above. Still there are lot of deficiencies in the model, first of all the model mentioned in [8,13] are tested from single PEMFC system to various PEMFC stack with different power as mentioned above.…”

Section: Introductionmentioning

confidence: 99%

“…Still there are lot of deficiencies in the model, first of all the model mentioned in [8,13] are tested from single PEMFC system to various PEMFC stack with different power as mentioned above. However the model in [19] has never been tested for single PEMFC system. Also this model is implemented on just two PEMFCs i.e Horizon 300 W and Nexa 1200 W PEMFC system in [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the novel dynamic semiempirical model of proton exchange membrane fuel cell by incorporating ambient condition variations

Abbou

Hasnaoui²,

Khan

et al. 2021

Int J Energy Environ Eng

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Proton exchange membrane fuel cell PEMFC is nonlinear source so modeling its output is essential. Also PEMFC is extremely prone to change in ambient conditions. Previously the complex modeling techniques have been used to model PEMFC voltage, but these technique are not good for online purposes. Hence a semiempirical approach has been suggested for PEMFC voltage prediction which uses the combination of theoretical and empirical equations. But most of the semiempirical models have lot of deficiencies which need to be corrected with the help of experimentation of different PEMFC systems at different conditions. In this research paper, a novel semiempirical model has been chosen which is less complex, dynamic and has the ability to diagnose fault as well. The model is only tested on two PEMFC stacks but never tested on single PEMFC. In this research, the model has been tested on single-cell PEMFC system and the parameters are optimized by using lightening search algorithm. The temperature and voltage model have been validated, and the new optimized parameters are recorded for different ambient conditions. The model discrepancies have been identified, and the new equations for parameters have been proposed which can be helpful in the making of generic model.

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Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

Cited by 11 publications

References 86 publications

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Performance prediction of fuel cells using long short‐term memory recurrent neural network

Acoustical Characteristics of Proton Exchange Membrane Fuel Cells

Analysis of the novel dynamic semiempirical model of proton exchange membrane fuel cell by incorporating ambient condition variations

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