Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Wang, Fu‐Kwun; Huang, Chang-Yi; Mamo, Tadele; Cheng, Xiao‐Bin

doi:10.1002/qre.2718

Cited by 14 publications

(10 citation statements)

References 42 publications

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“…Because of their clean and efficient functioning, PEMFC stacks are frequently employed in mobile and portable applications. To enhance the deterioration prediction of a PEMFC stack, FK Wang et al presented an ensemble model based on a stacked extended shortterm memory model that integrates three machine-learning models, including long shortterm memory with attention mechanism, support vector regression, and random forest regression [110]. The dropout approach was used to calculate the prediction intervals.…”

Section: Random Forestmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.

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Section: Random Forestmentioning

confidence: 99%

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

et al. 2022

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“…In recent years, three types of PEMFC prognostic strategies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][24][25][26][27][28][29][30][31][32][33] have been proposed: model-based, data-based, and hybrid methods based on model and data fusion. Among them, the model-based strategy describes the degradation processes by constructing a physical model [6][7][8], which is beneficial to the prediction accuracy in the condition that the physical degradation model is sufficiently accurate.…”

Section: Introductionmentioning

confidence: 99%

“…Among different DNN structures, long short-term memory (LSTM), a recurrent neural network (RNN) paradigm, has been considered as a potentially effective tool to handle time series prediction problems [14][15][16][17][18][19][20][24][25][26][27][28][29][30][31][32][33]. Several recent studies have been proposed to explore the LSTM application in fuel cell prognostics.…”

Section: Introductionmentioning

confidence: 99%

“…In these studies, the LSTM is deployed for either single-step or multi-step prediction (realized by implementing single-step prediction iteratively). Meanwhile, the degradation trend can be effectively predicted only for a short horizon of a few hours [14][15][16][17][18][30][31][32][33]. To achieve RUL estimation, multi-step prediction with long prediction horizon is required, particularly for the prognostics of aging degradation [19,20,[24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Wang

Outbib

et al. 2022

International Journal of Hydrogen Energy

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“…26 One application in fuel cell research is prediciting the stability of fuel cells. 27,28 However, machine learning has also been employed to optimize the synthesis protocol to construct Ni-rich cathode materials for Li-ion batteries. 29 Lately, machine learning has been used to optimize the PtPdAu alloy for methanol oxidation.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship between the CO Oxidation Reaction and the Elemental Composition in Electrocatalysts with Machine Learning - a Case Study of PtRuPdRhAu High Entropy Alloys

Mints

Pedersen

Bagger

et al. 2021

Preprint

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In recent years, the development of complex multi-metallic nanomaterials like high entropy alloy (HEA) catalysts has gained popularity. Composed of 5 or more metals, the compositions of HEAs exhibit extreme diversity. This is both a promising avenue to identify new catalysts and a severe constraint on their preparation and study. To address the challenges related to the preparation, study and optimization of HEAs, machine learning solutions are attractive. In this paper, the composition of PtRuPdRhAu hydrogen oxidation catalysts is optimized for the CO oxidation reaction. This is achieved by constructing a dataset using Bayesian optimization as guidance. For this quinary nanomaterial, the best performing composition was found within the first 35 experiments. However, the dataset was expanded until a total of 68 samples were investigated. This final dataset was used to construct a random forest regression model and a linear model. These machine learned models were used to assess the relationships between the concentrations of the consituent elements and the CO oxidation reaction onset potential. The onset potentials were found to correlate with the composition dependent adsorption energy of *OH obtained from density functional theory. This study demonstrates, how machine learning can be employed in an experimental setting to investigate the vast compositional space of HEAs.

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Ensemble model for the degradation prediction of proton exchange membrane fuel cell stacks

Cited by 14 publications

References 42 publications

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Exploring the Relationship between the CO Oxidation Reaction and the Elemental Composition in Electrocatalysts with Machine Learning - a Case Study of PtRuPdRhAu High Entropy Alloys

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