Static and Dynamic Modeling of a PEMFC for Prognostics Purpose

Lechartier, Élodie; Gouriveau, Rafael; Péra, Marie‐Cécile; Hissel, Daniel; Zerhouni, Noureddine

doi:10.1109/vppc.2014.7007136

Cited by 12 publications

(20 citation statements)

References 19 publications

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“…The values of constants L 1 to L 6 that are used in Equations (14) to (16) were obtained by applying the LSA to the 1.2-kW NEXA PEMFC system, as summarised in Table 2. The values of constants L 1 to L 6 that are used in Equations (14) to (16) were obtained by applying the LSA to the 1.2-kW NEXA PEMFC system, as summarised in Table 2.…”

Section: Semi-empirical Temperature Modelmentioning

confidence: 99%

“…However, many of the existing models [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] are highly complex and require the utilisation of sophisticated and expensive testing facilities in the laboratory. However, many of the existing models [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] are highly complex and require the utilisation of sophisticated and expensive testing facilities in the laboratory.…”

Section: Introductionmentioning

confidence: 99%

“…59 This research also emphasises the use of humidity sensors inside the PEMFC to aid fault diagnosis. 16 Another approach involved calculating the water content of the membrane, 72 but this requires the volume of the anode and cathode along with the dry density and weight of the membrane, which are not available for all commercial PEMFCs. Damoura et al proposed a fault diagnostic technique, which is based on signal processing, 63 and which involves empirical mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Section: Semi-empirical Temperature Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…. This simplified model has been verified to describe well the degradation process of a PEMFC . Thus, it is used in Section IV for generating synthetic degradation data for the case study.…”

Section: Characteristics Of the Considered Multistack Systemmentioning

confidence: 99%

“…Because of the complexity, multiphysics, multifailure, and multiscale characteristics of the PEMFC system, it is not realistic to build a precise model of degradation and aging process. Empirical and simplified physical models are proposed in Refs for modeling the degradation process. Extended Kalman filtering and Particle Filtering (PF) are two popular approaches for remaining useful life prediction based on the physical models.…”

Section: Introductionmentioning

confidence: 99%

Prognostics of a multistack PEMFC system with multiagent modeling

Liu

Zio

2018

Energy Science & Engineering

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Current research on prognostics of PEMFC mainly focuses on one stack. However, in practice, the PEMFC stacks are assembled together to provide higher and more reliable power. In this paper, an original multiagent approach is proposed for predicting the remaining useful life‐times of all stacks in a multistack PEMFC system. The predictions are updated dynamically by particle filtering when new stack loads are collected. The major contribution of the paper is not on degradation modeling of a single PEMFC, but on the cooperative prognostics of multiple PEMFC stacks in the same power system. A case study concerning a synthetic combined heat and power system with three PEMFC stacks is considered and the results show the effectiveness of the proposed approach.

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Dynamic Semiempirical PEMFC Model for Prognostics and Fault Diagnosis

et al. 2021

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This paper introduces a dynamic semiempirical model that predicts the degradation of a proton exchange membrane fuel cell (PEMFC) by introducing time-based terms in the model. The concentration voltage drop is calculated using a new statistical equation based on the load current and working time, whereas the ohmic and activation voltage drops are updated using time-based equations borrowed from the existing literature. Furthermore, the developed model calculates the membrane water content in the PEMFC, which indicates the membrane hydration state and indirectly diagnoses the flooding and drying faults. Moreover, the model parameters are optimized using a recently developed butterfly optimization algorithm. The model is simple and has a short runtime; therefore, it is suitable for monitoring. Voltage degradation under various loading currents was observed for long working hours. The obtained results indicate a significant degradation in PEMFC performance. Therefore, the proposed model is also useful for prognostics and fault diagnosis. INDEX TERMS Fault diagnostics, optimization, PEMFC, prognostics, statistical analysis Engineering for Smart & Sustainable Systems Research Department at Mohammadia School of Engineering (Rabat/Morocco). His scientific activities have mostly focused on fuel cells as a solution of storage of energy (more exactly, developing new methods for modeling and control of fuel cells).

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Static and Dynamic Modeling of a PEMFC for Prognostics Purpose

Cited by 12 publications

References 19 publications

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

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

Prognostics of a multistack PEMFC system with multiagent modeling

Dynamic Semiempirical PEMFC Model for Prognostics and Fault Diagnosis

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