Diagnosis of proton exchange membrane fuel cell system based on adaptive neural fuzzy inference system and electrochemical impedance spectroscopy

Ao, Yunjin; Laghrouche, Salah; Depernet, Daniel

doi:10.1016/j.enconman.2022.115391

Cited by 34 publications

(10 citation statements)

References 36 publications

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“…In this study, a more accurate ECM, named the 3-CPE model [39], is applied, as illustrated in Figure 1c. The impedances of the anode, cathode, and mass diffusion, alongside the membrane resistance, are used to represent the PEMFC impedance.…”

Section: Ecmmentioning

confidence: 99%

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Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

Ao,

Liu,

Laghrouche

et al. 2024

Sustainability

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The proton exchange membrane fuel cell (PEMFC) stands at the forefront of advancing energy sustainability. Effective monitoring, control, diagnosis, and prognosis are crucial for optimizing the PEMFC system’s sustainability, necessitating a dynamic model that can capture the transient response of the PEMFC. This paper uses a dynamic fractional-order model to describe the behaviors of a stationary micro combined heat and power (mCHP) PEMFC stack. Based on the fractional-order equivalent circuit model, the applied model accurately represents the electrochemical impedance spectroscopy (EIS) and the dynamic voltage response under transient conditions. The applied model is validated through experiments on an mCHP PEMFC stack under various fault conditions. The EIS data is analyzed under different current densities and various fault conditions, including the stoichiometry of the anode and cathode, the stack temperature, and the relative humidity. The dynamic voltage response of the applied model shows good correspondence with experimental results in both phase and amplitude, thereby affirming the method’s precision and solidifying its role as a reliable tool for enhancing the sustainability and operational efficiency of PEMFC systems.

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Section: Ecmmentioning

confidence: 99%

“…To this end, the genetic algorithm (GA) is used for identifying approximate global optimal parameters. This step is then followed by the application of the Levenberg-Marquardt (LM) method to achieve precise parameter refinement [39].…”

Section: Parameters Identificationmentioning

confidence: 99%

Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

Ao,

Liu,

Laghrouche

et al. 2024

Sustainability

Self Cite

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“…There are more parameters and SOC estimating techniques in the literature. The internal electrochemical reactions of the battery have frequently used electrochemical impedance spectroscopy [41]. The internal resistance technique uses rapid voltage and current readings to measure the battery's capacity.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

et al. 2023

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This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

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“…Mixing several techniques [25] employs an equivalent circuit model that was identified by combining a genetic algorithm (GA) and the Levenberg-Marquardt (LM) algorithm. For fault diagnosis, a method based on Adaptive Neural Fuzzy Inference System (ANFIS) and Electrochemical Impedance Spectroscopy (EIS) was applied to a 12-cell 196 cm2 stack to which the stoichiometric ratio and air humidity would be varied to produce membrane flooding and drying.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are applied to a single cell and are developed under controlled laboratory conditions, where temperature and pressures are managed. Some notable works in this area include: [6][7][8][13][14][15][16][17]24,25].…”

Section: Introductionmentioning

confidence: 99%

PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion

Ariza,

Correcher,

Vargas-Salgado

2023

Sensors

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Proton Exchange Membrane Fuel Cells (PEMFCs) are critical components in renewable hybrid systems, demanding reliable fault diagnosis to ensure optimal performance and prevent costly damages. This study presents a novel model-based fault diagnosis algorithm for commercial hydrogen fuel cells using LabView. Our research focused on power generation and storage using hydrogen fuel cells. The proposed algorithm accurately detects and isolates the most common faults in PEMFCs by combining virtual and real sensor data fusion. The fault diagnosis process began with simulating faults using a validated mathematical model and manipulating selected input signals. A statistical analysis of 12 residues from each fault resulted in a comprehensive fault matrix, capturing the unique fault signatures. The algorithm successfully identified and isolated 14 distinct faults, demonstrating its effectiveness in enhancing reliability and preventing performance deterioration or system shutdown in hydrogen fuel cell-based power generation systems.

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Diagnosis of proton exchange membrane fuel cell system based on adaptive neural fuzzy inference system and electrochemical impedance spectroscopy

Cited by 34 publications

References 36 publications

Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

Dynamic Fractional-Order Model of Proton Exchange Membrane Fuel Cell System for Sustainability Improvement

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion

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