Evaluation Method for Feature Selection in Proton Exchange Membrane Fuel Cell Fault Diagnosis

Mao, Lei; Liu, Zhongyong; Low, Daniel J.; Pan, Weitao; He, Qingbo; Jackson, Lisa M.; Wu, Qiang

doi:10.1109/tie.2021.3078395

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…From these features, the spatial distribution characteristic of voltage can be revealed because the positions of the cells will greatly affect its voltage output. However, the mechanism of the voltage spacial distribution is still not clear, and it depends on the structure of the PEMFC [10].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of PEMFC based on autoregressive model and voltage fluctuation

Laghrouche

Depernet

et al. 2022

2022 10th International Conference on Systems and Control (ICSC)

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A novel diagnosis approach for proton exchange membrane fuel cell (PEMFC) systems is proposed in this paper. Different fault conditions can be classified based on the patterns of stack voltage fluctuation, which can be extracted by the autoregressive model (AR model). The proposed method focuses on the stack voltage fluctuation over time, thus it is more practical and less complex as only the stack voltage needs to be collected. The AR model is employed to extract voltage fluctuation features, and then several widely applied classifiers are applied to classify fault conditions. Experiments are carried out to demonstrate the effectiveness. Those faults are introduced by the adjustment of anode stoichiometry, cathode stoichiometry, relative humidity level, and the cooling circuit temperature. The diagnostic accuracy for single-fault conditions is 99%, while it is 93% for multi-fault conditions. Also, compared to the singularity analysis method in our former research, the proposed method is more time-saving. Moreover, the voltage sampling frequency and sample window length are adjusted to research the diagnosis effectiveness, which is studied and discussed for the first time.

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Section: Introductionmentioning

confidence: 99%