Study of degradation of fuel cell stack based on the collected high-dimensional data and clustering algorithms calculations

Niu, Tong; Huang, Weifeng; Zhang, Caizhi; Zeng, Tao; Chen, Jiawei; Liu, Yu; Liu, Yang

doi:10.1016/j.egyai.2022.100184

Cited by 36 publications

(14 citation statements)

References 34 publications

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“…(3) Outliers: A sample that is neither a core point nor a border point [54,57]. Figure 4 illustrates the process of the DBSCAN clustering algorithm [31,58,59], where A is a randomly selected point in the sample points.…”

Section: Density-based Clusteringmentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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Section: Density-based Clusteringmentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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“…Bacteria such as E. coli , Geobacter spp., and Pseudomonas spp. are efficiently and actively fixed to the surface of the anode, allowing efficient and direct electron transfer [ 16 ]. Since biological reactions take place on the surfaces of the anode, the surface area has a significant impact on the performance of the MFC [ 17 ].…”

Section: The Anodementioning

confidence: 99%

“…The quality and properties of the anode have a direct impact on energy generation [ 21 ]. Carbon, graphite, metal, metal oxides, and natural waste materials have been used as anodes in MFCs [ 16 , 22 , 23 ].…”

Section: The Anodementioning

confidence: 99%

“…Carbon materials are commonly used to fabricate anode because of their high conductivity, large surface area, low porosity, good biocompatibility, high chemical and thermal stability, ease of availability, and good electron transfer kinetics. Carbon materials were commonly used for anodes, such as carbon nanotubes (CNTs), carbon cloth, carbon paper, carbon rods, carbon meshes, carbon brushes, carbon felt, carbon fibers, and reticulated vitreous carbon [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Carbon cloth is the most used to fabricate the anode in MFCs.…”

Section: The Anodementioning

confidence: 99%

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Applications of Nanomaterials in Microbial Fuel Cells: A Review

et al. 2022

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Microbial fuel cells (MFCs) are an environmentally friendly technology and a source of renewable energy. It is used to generate electrical energy from organic waste using bacteria, which is an effective technology in wastewater treatment. The anode and the cathode electrodes and proton exchange membranes (PEM) are important components affecting the performance and operation of MFC. Conventional materials used in the manufacture of electrodes and membranes are insufficient to improve the efficiency of MFC. The use of nanomaterials in the manufacture of the anode had a prominent effect in improving the performance in terms of increasing the surface area, increasing the transfer of electrons from the anode to the cathode, biocompatibility, and biofilm formation and improving the oxidation reactions of organic waste using bacteria. The use of nanomaterials in the manufacture of the cathode also showed the improvement of cathode reactions or oxygen reduction reactions (ORR). The PEM has a prominent role in separating the anode and the cathode in the MFC, transferring protons from the anode chamber to the cathode chamber while preventing the transfer of oxygen. Nanomaterials have been used in the manufacture of membrane components, which led to improving the chemical and physical properties of the membranes and increasing the transfer rates of protons, thus improving the performance and efficiency of MFC in generating electrical energy and improving wastewater treatment.

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“…Based on the literature survey, the phenomena of two-phase flow, non-isothermal effect, and convection have not been simultaneously and comprehensively incorporated in a 1D fuel cell model yet, and the computing speed of 1D models needs to be improved for control applications. It should be also noted that many data-driven models [46][47][48] have been developed for the fuel cell control and do not require any physical knowledge of the fuel cell stack. However, data-driven models require a high-volume and high-quality experimental dataset and can be fuel cell specific, which is out of the scope of the present study.…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

Zhao

Li²,

Shum³

et al. 2023

J. Phys. Energy

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The performance of a proton exchange membrane (PEM) fuel cell is determined by many factors, including operating conditions, component specifications, and system design, making it challenging to predict its performance over a wide range of operating conditions. Existing fuel cell models can be complex and computationally demanding or may be over-simplified by neglecting many transport phenomena. Therefore, a high-fidelity and computationally efficient model is urgently needed for the model-based control of fuel cells. In this study, semi-implicit multi-physics numerical models have been established, taking the mass, momentum, reactants, liquid water, membrane water, electrons, ions, and energy in all fuel cell components into account. The developed 1D model is of high fidelity by incorporating the two-phase flow, non-isothermal effect, and convection, and is still computationally efficient. These models are validated against data from an auto manufacturer with good agreements, and the computing efficiency is evaluated on a modest laptop computer. The modeling results suggest that the two-phase flow model exhibits better prediction accuracy than the single-phase flow model when reactants are fully humidified, while under low humidity conditions, the two models present equivalent performance as liquid water does not exist in the fuel cell components. The results also suggest that the maximum convective/diffusive ratio of H2, O2, and vapor mass fluxes can be 12%, 5.3%, and 35%, respectively, which are ignored in most diffusion-dominant models. The developed models are computationally efficient, requiring only 0.56 s and 0.26 s to simulate a steady-state operation of fuel cells for the two- and single-phase flow models, respectively. This implies that the developed models are suitable for the control of PEM fuel cells.

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Study of degradation of fuel cell stack based on the collected high-dimensional data and clustering algorithms calculations

Cited by 36 publications

References 34 publications

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Applications of Nanomaterials in Microbial Fuel Cells: A Review

A computationally efficient and high-fidelity 1D steady-state performance model for PEM fuel cells

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