Effects of Endplate Assembly on Cold Start Performance of Proton Exchange Membrane Fuel Cell Stacks

Wei, Lin; Liao, Zihao; Dafalla, Ahmed Mohmed; Suo, Zhenbang; Jiang, Fangming

doi:10.1002/ente.202100092

Cited by 11 publications

(6 citation statements)

References 50 publications

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“…They found the stack had a better performance than the single cell in the cold start, the middle cell in the stack had better performance than the end cell, and the consistency of stack became worse after the cold start failed. Wei et al 30 established a three-dimensional multi-phase transient model of the stack, and studied the effects of the end plate on the performance of the stack during the cold start process by analyzing the distribution of water, ice, the current density and the temperature in the stack. They found that the end plate had a great influence on the stack under the condition of −30°C, which directly causing the end plate side cells to be completely blocked by ice.…”

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confidence: 99%

End Plate Effects on the Performance of PEMFCs During Cold Start Process

Wan

Zhan

Jiang

et al. 2021

J. Electrochem. Soc.

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The efficient, fast, and reliable cold start of polymer electrolyte membrane fuel cells is one of the major challenges for their commercialization. In this paper a segmented single cell is used to simulate the end plate effects of the stack and to investigate how the effects work. The results demonstrate that the end cells in the stack have the lowest performance, the reasons for which include the lowest temperature of the cells themselves, and probably also ice blocking in part an area inside the MEA, or in the channels, or in both together. In order to mitigate or even eliminate the influence of ice formation in the end cells, the temperature of the end plates should be increased to -10℃ or above when liquid water is generated. A high inlet gas flow rate facilitates the discharge of supercooled water and is conducive to successful cold start.

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mentioning

confidence: 99%

End Plate Effects on the Performance of PEMFCs During Cold Start Process

Wan

Zhan

Jiang

et al. 2021

J. Electrochem. Soc.

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“…The problem with cold starts occurs in the system when recommending accurately becomes challenging with new users who lack rating data or have sparse data. This problem cannot be ignored during operation, making it a difficult issue to address [21] . The issue of data sparsity is predominantly a result of the increasing number of users and data stored over time.…”

Section: A Cf-based Book Recommendation Model For University Librariesmentioning

confidence: 99%

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

Cui,

Gao

2024

SCPE

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In today’s information era, collaborative filtering algorithms are widely used and their distinct knowledge discovery techniques can effectively address numerous issues. However, conventional collaborative filtering algorithms encounter cold-start and data sparsity issues, which restrict their performance and accuracy. The study selected the multi-feature method to improve the traditional collaborative filtering algorithm, and introduced gray correlation calculation and Bayesian probability for user preference analysis. A learning resource recommendation model based on collaborative filtering was developed by comparing the target user’s characteristics with those of other users, calculating their similarity, selecting users with high similarity to the target user and forming a neighbor set. Using Bayesian probability and grey correlation to analyze user preferences in library systems can be well applied in book classification and recommendation problems in university libraries. The computing layer, which includes the collaborative filtering calculation stage and the group recommendation calculation stage, is the model’s main functional component. The smaller the value of mean absolute error, the higher the prediction accuracy of the model. The mean absolute error value of the multi-feature collaborative filtering algorithm was inferior to the traditional collaborative filtering algorithm, indicating that the classification accuracy of the former is higher than that of the latter. When the training set to test set ratio steadily became bigger, the mean absolute error value reached the lowest and smoothest point at 80%. In dataset A, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.765 and 0.809. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.044. In dataset B, the mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.796 and 0.836. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.040. In dataset C, the minimum mean absolute error values of multi-feature collaborative filtering and collaborative filtering were 0.815 and 0.848. Compared with traditional filtering algorithms, the mean absolute error value has decreased by 0.033. When the accuracy was the highest; the mean absolute error value was the smallest at the grey correlation, which means that the technique improves the reliability of the recommendations compared with other methods. This means that the method has a positive impact on the accuracy of the recommendations compared to other methods. Grey correlation degree can comprehensively consider the interrelationships between multiple factors, handle uncertain and incomplete information, and explore potential user needs and behavior patterns. The implementation of the grey correlation degree has transformed the collaborative filtering algorithm into a group filtering algorithm, thereby enhancing its precision. The research on book classification and recommendation in university libraries, which enhances the group filtering algorithm, can address a range of issues such as improving classification accuracy, augmenting recommendation diversity, enhancing library management efficiency among others. This, in turn, enables more precise book recommendations to users.

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“…The present 3D PEMFC model resorts to fuel cell computational dynamics and is based on our previous work. [ 35 ] It has the capability of analyzing the coupled transport and reaction processes and, to understand the possibility of operating without external humidification of the reactant gases, the dry operation condition. [ 36 ] It has been shown that only a small amount of liquid exists near the outlet of the channel under low humidification operation.…”

Section: Model Descriptionmentioning

confidence: 99%

Performance Investigation of Proton‐Exchange Membrane Fuel Cell with Dean Flow Channels

et al. 2022

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Herein, a novel fuel cell flow field with 3π/2 radian Dean flow channels and its impact on the performance of proton exchange membrane fuel cells are presented. With a multidisciplinary numerical model, flow characteristics, concentration distribution, and current density contour in the fuel cell are compared between straight and Dean flow channel cases. The unstable vortices formed in the Dean flow channels are shown to enhance mass transfer of both reactants and water. As a result, although the voltage under low current density is slightly lower than that of fuel cells with straight channels, the output performance under high current density is observably improved. It is also found that the enhancement of oxygen transport with the Dean channel is more significant at relatively small cathode stoichiometry. In addition, by reducing the gas diffusion layer porosity, the membrane is kept at high humidity, yielding an improvement in power output and stability of the fuel cell at low cathode inlet pressure operation.

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Effects of Endplate Assembly on Cold Start Performance of Proton Exchange Membrane Fuel Cell Stacks

Abstract: Figure 1. a) Schematic diagram of a PEMFC stack and b) mesh of the computed geometry.

Cited by 11 publications

References 50 publications

End Plate Effects on the Performance of PEMFCs During Cold Start Process

End Plate Effects on the Performance of PEMFCs During Cold Start Process

Book Classification and Recommendation for University Libraries using Grey Correlation and Bayesian Probability

Performance Investigation of Proton‐Exchange Membrane Fuel Cell with Dean Flow Channels

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