A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures

Zhang, Caizhi; Zhang, Yuqi; Wang, Lei; Deng, Xiaozhao; Yang, Lei; Zhang, Jiujun

doi:10.1016/j.rser.2023.113369

Cited by 51 publications

(6 citation statements)

References 184 publications

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“…84 In addition, due to the different coefficients of linear expansion between the coated metal and the substrate, the thermal cycling oen leads to the formation of micro-cracks during operating cycles. 169 Therefore, it is still necessary to explore a more advanced coating technology in the future to achieve better corrosion resistance, lower contact resistance, and lower coating costs for metal BPs. 24,145 In addition to metal BPs, composite materials are oen used as special materials in preparing BPs.…”

Section: Bipolar Plates (Bps) In Pemfcs With High Specific Power Densitymentioning

confidence: 99%

Designing proton exchange membrane fuel cells with high specific power density

Zhao

Tao

et al. 2023

J. Mater. Chem. A

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Section: Bipolar Plates (Bps) In Pemfcs With High Specific Power Densitymentioning

confidence: 99%

Designing proton exchange membrane fuel cells with high specific power density

Zhao

Tao

et al. 2023

J. Mater. Chem. A

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“…In Eq. [3], 𝛽 is called anisotropy parameter. 𝛽 = means that all the carbon fibers are aligning parallel to the TP direction.…”

Section: Periodic Surface Modelsmentioning

confidence: 99%

“…Thus, an orientation-control PDF function is introduced to the periodic surface model, see Eq. [3], to investigate its effect on GDL properties. It should be mentioned that only the bulk porosity is utilized in this part.…”

Section: Anisotropymentioning

confidence: 99%

“…A Proton Exchange Membrane Fuel Cell (PEMFC) is widely recognized as a clean and efficient power generation device, with zero pollution emissions. It holds significant promise for a diverse range of applications, including portable, stationary, and transportation sectors (1)(2)(3). One crucial component of the PEMFC is the Gas Diffusion Layer (GDL), typically composed of carbon fiber, additional binder, and hydrophobic agents like polytetrafluoroethylene (4,5).…”

Section: Introductionmentioning

confidence: 99%

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Numerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers

Yang,

Garg,

Beale

et al. 2023

ECS Trans.

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Stochastic reconstruction is widely employed for effective and flexible imitation of Gas Diffusion Layers (GDLs), e.g., to facilitate the study of their properties. However, the reconstruction often overlooks crucial factors such as fiber curvature, fiber stack arrangement, and fiber anisotropy. Consequently, the impact of these structural characteristics remains poorly understood. In this study, an in-house reconstruction procedure is developed based on the periodic surface model. This procedure enables the generation of GDLs with either straight or curved fibers, layer-by-layer or random arrangement, and different probabilities of through-plane fiber orientation angles. The porosity, domain size, and fiber diameter are extracted from an experimental image-based GDL and utilized as input data for the reconstruction. Furthermore, the different GDLs are compared in terms of pore size distribution and through-plane porosity distribution. It is concluded that introducing proper selections of these fiber features gives the reconstruction more realistic properties.

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“…At present, according to relevant research in the literature both at home and abroad, the model-driven method [2][3][4][5][6], data-driven method [7][8][9][10][11][12], and hybrid model [13][14][15][16][17] method are the main methods for the RUL prediction of PEMFC. The model-driven method uses the empirical model or the mechanism model for the RUL of PEMFC [18,19]. Koltsova [20] proposed an electrochemical reaction area decay mechanism model to predict the RUL of the reactor through the decay of the electrochemical reaction area.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model

Tian,

Chen,

Ding

et al. 2023

Electronics

Self Cite

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To predict the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC) in advance, a prediction method based on the voltage recovery model and Bayesian optimization of a multi-kernel relevance vector machine (MK-RVM) is proposed in this paper. First, the empirical mode decomposition (EMD) method was used to preprocess the data, and then MK-RVM was used to train the model. Next, the Bayesian optimization algorithm was used to optimize the weight coefficient of the kernel function to complete the parameter update of the prediction model, and the voltage recovery model was added to the prediction model to realize the rapid and accurate prediction of the RUL of PEMFC. Finally, the method proposed in this paper was applied to the open data set of PEMFC provided by Fuel Cell Laboratory (FCLAB), and the prediction accuracy of RUL for PEMFC was obtained by 95.35%, indicating that this method had good generalization ability and verified the accuracy of the method when predicting the RUL of PEMFC. The realization of long-term projections for PEMFC RUL not only improves the useful life, reliability, and safety of PEMFC but also reduces operating costs and downtime.

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A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures

Cited by 51 publications

References 184 publications

Designing proton exchange membrane fuel cells with high specific power density

Designing proton exchange membrane fuel cells with high specific power density

Numerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers

Remaining Useful Life Prediction Method of PEM Fuel Cells Based on a Hybrid Model

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