Proton membrane fuel cell stack performance prediction through deep learning method

Fu, Jiangtao; Fu, Zhumu; Song, Shuzhong

doi:10.1016/j.egyr.2022.04.015

Cited by 11 publications

(1 citation statement)

References 26 publications

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“…To promote the commercial application of PEMFC, the US Department of Energy (DOE) has formulated the corresponding standard, where the life of PEMFC should reach 8000 h in 2025, and the maximum output power of the reactor should ensure that the maximum output power cannot be less than 90% of the rated output power [1]. 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].…”

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

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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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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

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Multi‐step performance degradation prediction method for proton‐exchange membrane fuel cell stack using 1D convolution layer and CatBoost

Zhang,

Dong,

et al. 2024

Adaptive Control & Signal

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SummaryThe increasing environmental issues such as climate change and air pollution require energy saving and emission reduction in various fields, such as manufacturing, building, and transportation. To address the above problem, proton‐exchange membrane fuel cells (PEMFC) gradually become promising green energy conversion device due to the advantages of zero pollution, high efficiency, and low operating noise. However, the durability problem has extremely limited the PEMFC large‐scale commercial application. To prolong the service life of PEMFC, performance degradation prediction is an effective method. This paper proposes a multi‐step performance degradation prediction method for proton‐exchange membrane fuel cells based on CatBoost feature selection, convolution computing, and interactive learning mechanism. CatBoost is used to evaluate the importance of the monitor parameters on performance degradation. The evaluation results and PEMFC degradation mechanism analyses are used to select the monitor parameters for construing the prediction model. Based on the 1D convolutional layer and the interactive learning mechanism, the prediction model is proposed to extract the deep features from the monitor data to predict the performance degradation of the fuel cell system. In particular, the multi‐step prediction is performed by the configurable sliding window. The effectiveness of the proposed method is verified on real experiment datasets, and the experiment results show that the proposed method is particularly effective for multi‐step degradation prediction and decreases the computation by feature selection and 1D convolution layer.

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Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC

et al. 2023

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Proton membrane fuel cell stack performance prediction through deep learning method

Cited by 11 publications

References 26 publications

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

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

Multi‐step performance degradation prediction method for proton‐exchange membrane fuel cell stack using 1D convolution layer and CatBoost

Evolutionary gate recurrent unit coupling convolutional neural network and improved manta ray foraging optimization algorithm for performance degradation prediction of PEMFC

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