Proton Exchange Membrane Fuel Cell Parameter Extraction Using a Supply–Demand-Based Optimization Algorithm

Al-Shamma’a, Abdullrahman A.; Ali, Fekri Abdulraqeb Ahmed; Alhoshan, Mansour; Alturki, Fahd A.; Farh, Hassan M. H.; Alam, Javed; AlSharabi, Khalil

doi:10.3390/pr9081416

Cited by 15 publications

(14 citation statements)

References 56 publications

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“…The activation losses occur due to the kinetics of the reactions taking place at the electrode. They can be calculated using Equation (10) [34].…”

Section: The Activation Polarizationmentioning

confidence: 99%

“…where the parameters ζ 1 , ζ 2 , ζ 3 and ζ 4 are parametric coefficients determined by the constructor, I is the current of the PEMFC, and C O2 is the oxygen concentration in the catalysts (mol• cm −3 ) and it could be calculated using Equation ( 11) [34,35].…”

Section: The Activation Polarizationmentioning

confidence: 99%

“…They have two origins: the internal resistance of the electrolyte membrane R mem and the resistance that occurred due to the contact between the bipolar plates and the carbon electrodes R con . These losses can be calculated using Equation (12) [34]:…”

Section: The Ohmic Lossesmentioning

confidence: 99%

“…The parameter σ mem is the specific resistance of the membrane (Ω • cm), A is the single cell active surface in cm 2 , l is the membrane thickness in (cm). The following expression for the specific resistance is used [34,36]:…”

Section: The Ohmic Lossesmentioning

confidence: 99%

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Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

et al. 2022

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This paper presents a comparison of optimizers for tuning a fractional-order proportional-integral-derivative (FOPID) and proportional-integral-derivative (PID) controllers, which were applied to a DC/DC boost converter. Grey wolf optimizer (GWO) and extended grey wolf optimizer (EGWO) have been chosen to achieve suitable parameters. This strategy aims to improve and optimize a proton exchange membrane fuel cell (PEMFC) output power quality through its link with the boost converter. The model and controllers have been implemented in a MATLAB/SIMULINK environment. This study has been conducted to compare the effectiveness of the proposed controllers in the transient, accuracy in tracking the reference current, steady-state, dynamic responses, overshoots, and response time. Results showed that the combination EGWO-FOPID had significant advantages over the rest of the optimized controllers.

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“…The activation losses occur due to the kinetics of the reactions taking place at the electrode. They can be calculated using Equation (10) [34].…”

Section: The Activation Polarizationmentioning

confidence: 99%

Section: The Activation Polarizationmentioning

confidence: 99%

Section: The Ohmic Lossesmentioning

confidence: 99%

Section: The Ohmic Lossesmentioning

confidence: 99%

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Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

et al. 2022

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“…Additionally, the SDA adds the distinguishing features of exploratory and exploitative strategies, which are discriminated by controlling the supply and demand weights. The SDA has been applied effectively for different engineering optimization tasks, such as the performance evaluation of the proton-exchange membrane fuel cells [32], the sizing of hybrid energy systems [33], the photovoltaic cells' design [34], and parameter estimations of an induction motor [35].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

et al. 2022

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The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

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An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

Ayvaz

2022

Intl J of Energy Research

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Summary In this study, an improved variant of chicken swarm optimization (CSO), named I‐CSO, is proposed to find the unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. Although the basic CSO has a well‐established population hierarchy mechanism that gives it an important advantage over its competitors, it suffers from premature convergence and can be easily trapped into the local optima because of inadequate use of population information in the update rule of the rooster's position. In the proposed I‐CSO, this shortcoming is addressed by introducing a new learning strategy for the roosters, which play leadership roles in the foraging behavior of the chicken swarm, to improve the algorithm convergence capability. Moreover, an adaptive inertia weight is introduced to make the algorithm more stable by striking a better balance between the exploration and exploitation phase. The sum of absolute error between the actual and estimated voltage outputs of the stack is suggested as the objective function to perform the optimization. Besides the suggested one, two other objective functions are also used to evaluate the impact of objective function choice on the optimization results. The test of the method is performed on two commercial PEMFCs, which are BCS 500‐W Stack and NedStack PS6, and the results of I‐CSO are compared with those of other competitive algorithms published in the literature. The final results show that the use of the proposed I‐CSO with the suggested objective function demonstrates excellent performance in estimating the PEMFC model parameters with fewer errors.

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Proton Exchange Membrane Fuel Cell Parameter Extraction Using a Supply–Demand-Based Optimization Algorithm

Cited by 15 publications

References 56 publications

Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

Fractional Order PID Design for a Proton Exchange Membrane Fuel Cell System Using an Extended Grey Wolf Optimizer

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

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