A Fast Fault Diagnosis Method of the PEMFC System Based on Extreme Learning Machine and Dempster–Shafer Evidence Theory

Liu, Jiawei; Li, Qi; Chen, Weirong; Yan, Yu; Wang, Xiaotong

doi:10.1109/tte.2018.2886153

Cited by 84 publications

(34 citation statements)

References 35 publications

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“…In addition, U ohm increases linearly with the current density, and U act increases logarithmically. More details can be found in [11,12]. Thus, the cell voltage can be described as a function of temperature and current density as follows:…”

Section: Voltage Model Of 'T-h-h'mentioning

confidence: 99%

“…In addition, U ohm increases linearly with the current density, and U act increases logarithmically. More details can be found in [11, 12]. Thus, the cell voltage can be described as a function of temperature and current density as follows:

U_{cell} = f false(i_{e}, T_{e} false) = U_{rev} false(T_{e} false) + U_{act} false(i_{e}, T_{e} false) + U_{ohm} false(i_{e}, T_{e} false)

The equation suggests that the overall potential of the electrolyzer can be expressed by a circuit composed of three voltage sources, as shown in Fig.…”

Section: Model Of P2hhmentioning

confidence: 99%

“…However, the issue of energy efficiency is rarely studied in current research. Considering the commercial electrolyzers such as alkaline or PEM, the transform efficiency is approximately 60-70% [9][10][11]. The other energy is consumed by the auxiliary facilities or dissipated into the air.…”

Section: Introductionmentioning

confidence: 99%

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Optimal control of a hydrogen microgrid based on an experiment validated P2HH model

Lin

Zeng

et al. 2019

IET Renewable Power Generation

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Converting electricity into hydrogen (power-to-hydrogen-P2H) is proved to be an effective means of energy storage. However, the conversion efficiency of commercial electrolyzers is currently among 60-70% and the storage and transportation cost of hydrogen is relatively high. On this basis, this study promotes the comprehensive usage of P2H as a flexible load in renewable power systems as well as heat sources in the heat network. The model of power-to-heat and hydrogen (P2HH) is established, considering the utilisation of waste heat. This model has further experimentally verified with alkaline electrolyzer groups rated at 46.5 kW. The authors established an optimal control model of a P2HH microgrid and carried out a case study on this model; the results show that temperature plays an important role in the P2HH process because it directly influences the proportion of electricity to generate hydrogen and the proportion of electricity to generate heat. Besides, P2HH helps improve the overall system efficiency despite the possible efficiency reductions of the power to hydrogen process itself.

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Section: Voltage Model Of 'T-h-h'mentioning

confidence: 99%

“…In addition, U ohm increases linearly with the current density, and U act increases logarithmically. More details can be found in [11, 12]. Thus, the cell voltage can be described as a function of temperature and current density as follows:

U_{cell} = f false(i_{e}, T_{e} false) = U_{rev} false(T_{e} false) + U_{act} false(i_{e}, T_{e} false) + U_{ohm} false(i_{e}, T_{e} false)

The equation suggests that the overall potential of the electrolyzer can be expressed by a circuit composed of three voltage sources, as shown in Fig.…”

Section: Model Of P2hhmentioning

confidence: 99%

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Optimal control of a hydrogen microgrid based on an experiment validated P2HH model

Lin

Zeng

et al. 2019

IET Renewable Power Generation

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“…All these reasons have motivated the development of data-based methods. In data-based methods [20][21][22][23][24][25][26], the only information available is in the form of historical data corresponding to the different operating modes of the system. First, during a learning phase, an algorithm is trained to classify the historical data (often collected on a real fuel cell) into fault categories.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Isolation for Proton Exchange Membrane Fuel Cell Using Impedance Measurements and Multiphysics Modeling

et al. 2020

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This study proposes a fault detection and isolation tool for proton exchange membrane fuel cell (PEMFC) operating in embedded applications. A model-based approach, taking partially into account degradation phenomena, is proposed in order to increase the robustness of the tool regarding transient operations and stack ageing. The considered faults are the abnormal operating conditions that can decrease the fuel cell lifetime. The fault detection approach is based on residual generation using both voltage and high frequency resistance measurements and thus combining the advantages of knowledge-based model and electrochemical impedance spectroscopy (EIS) diagnosis approaches. To that end, a multi-physics fuel cell model has been used. This model computes not only the stack voltage but also the high frequency resistance in dynamic conditions. Additionally, the model is modified to take into account the ageing of the fuel cell. Validation is carried out on experimental characterizations during 1,000 h ageing. The results on a new fuel cell stack show a score of 91% for fault isolation. However, without any adaptation, this score drops dramatically as the stack ages. Finally, thanks to ageing modeling and to the proposed adaptation of the detection/isolation procedure, the diagnosis performance remains reliable during fuel cell stack ageing.

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“…59 This research also emphasises the use of humidity sensors inside the PEMFC to aid fault diagnosis. Other researchers proposed the use of artificial intelligence for fault diagnosis 46,[60][61][62] ; however, this technique may only be applicable to one type of PEMFC system and requires a huge amount of training data before it could be applied to other types of PEMFC system. Damoura et al proposed a fault diagnostic technique, which is based on signal processing, 63 and which involves empirical mode decomposition.…”

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confidence: 99%

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

et al. 2019

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A Fast Fault Diagnosis Method of the PEMFC System Based on Extreme Learning Machine and Dempster–Shafer Evidence Theory

Cited by 84 publications

References 35 publications

Optimal control of a hydrogen microgrid based on an experiment validated P2HH model

Optimal control of a hydrogen microgrid based on an experiment validated P2HH model

Fault Detection and Isolation for Proton Exchange Membrane Fuel Cell Using Impedance Measurements and Multiphysics Modeling

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

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