Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer

Agwa, Ahmed M.; El‐Fergany, Attia A.; Sarhan, Gamal M.

doi:10.3390/en12101884

Cited by 65 publications

(36 citation statements)

References 30 publications

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“…The typical I/V polarization curve of the PEMFC has three voltage declines based on the operating region of the cell such as: (a) v act at low current values, (b) v R which behaves non‐linearly, and (c) the v conc at higher loading conditions. So, the V S can be extracted using Equation (1) with the assumption that all connected cells have the same operating conditions

V_{S} = N_{cells} . ((), E_{N} - v_{act} - v_{R} - v_{conc})

…”

Section: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

“…In the case of PEMFCs, the reversible voltage is calculated using Equation (2) for temperatures ≤100°C. Then again, the other voltage declines as defined in Equation (1) are figured out by means of (6), (8), and (10), correspondingly

E = 1.229 - 0.85 . 10^{- 3} ((), T_{fc} - 298.15) + 4.3085 . 10^{- 5} T_{fc} italicln ((), P_{H_{2}} \sqrt{P_{O_{2}}})

P_{H_{2}} = \frac{{RH}_{a} . P_{H_{2} O}}{2} ([], 1 / ((), \frac{{RH}_{a} . P_{H_{2} O}}{P_{a}} . e^{\frac{1.635 I_{italicfc} / A}{{T_{italicfc}}^{1.334}}}) - 1)

P_{O_{2}} = {RH}_{c} . P_{H_{2} O} ([], 1 / ((), \frac{{RH}_{c} . P_{H_{2} O}}{P_{c}} . e^{\frac{4.192 I_{italicfc} / A}{{T_{italicfc}}^{1.334}}}) - 1)

P_{H_{2} normalO} = 2.95 . 10^{- 2} T_{c} - 9.18 . 10^{- 5} {normalT}_{normalc}^{2} + 1.44 . 10^{- 7} {normalT}_{normalc}^{3} - 2.18

and T_{c} =

…”

Section: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

“…Among these different types, PEMFC has several salient features like operating conditions including temperature and pressure, high performance, and no waste material . Its operating temperature lies in the range [50‐100°C] and the efficiency of PEMFC varies between 30% and 60% under environmental conditions . There are many applications of the PEMFC in industrial applications.…”

Section: Introductionmentioning

confidence: 99%

“…In this issue, various forms of meta‐heuristic frameworks are used to solve this optimization problem achieving an accurate model. These algorithms involve NNA, atom search, SSA, harmony search, JAYA, SSO, satin bowerbird optimizer, adaptive GA, DE, cuckoo search, hybrid bee colony, and GWO …”

Section: Introductionmentioning

confidence: 99%

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Parameters extraction of PEMFC's model using manta rays foraging optimizer

2020

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Summary In this article, a recently developed bio‐inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non‐linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic‐based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H‐12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.

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V_{S} = N_{cells} . ((), E_{N} - v_{act} - v_{R} - v_{conc})

…”

Section: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

E = 1.229 - 0.85 . 10^{- 3} ((), T_{fc} - 298.15) + 4.3085 . 10^{- 5} T_{fc} italicln ((), P_{H_{2}} \sqrt{P_{O_{2}}})

P_{H_{2}} = \frac{{RH}_{a} . P_{H_{2} O}}{2} ([], 1 / ((), \frac{{RH}_{a} . P_{H_{2} O}}{P_{a}} . e^{\frac{1.635 I_{italicfc} / A}{{T_{italicfc}}^{1.334}}}) - 1)

P_{O_{2}} = {RH}_{c} . P_{H_{2} O} ([], 1 / ((), \frac{{RH}_{c} . P_{H_{2} O}}{P_{c}} . e^{\frac{4.192 I_{italicfc} / A}{{T_{italicfc}}^{1.334}}}) - 1)

P_{H_{2} normalO} = 2.95 . 10^{- 2} T_{c} - 9.18 . 10^{- 5} {normalT}_{normalc}^{2} + 1.44 . 10^{- 7} {normalT}_{normalc}^{3} - 2.18

and T_{c} =

…”

Section: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameters extraction of PEMFC's model using manta rays foraging optimizer

2020

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show abstract

“…23 A DE convergence trend is enhanced by using two neighborhood search, dynamic crossover probability, and integrated scaling factor. 33 Many other heuristic methods have been utilized for solving the PEMFC problems such as salp swarm optimizer (SSO), 34 grasshopper optimization algorithm (GOA), 35 aging and exciting P-systems based optimization procedures using semi-empirical models, 9 bird mating optimizer, 36 atom search optimizer, 37 and shark smell algorithm (SSA). 25 A fast and precise parameter identification for different types of FCs with decomposition and recent optimization method is addressed.…”

Section: Introductionmentioning

confidence: 99%

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

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Parameters determination of proton exchange membrane fuel cell stack electrical model by employing the hybrid water cycle moth‐flame optimization algorithm

Messaoud

2020

Int J Energy Res

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In order to properly control the operation of a fuel cell (FC), it is essential to have a precise model of the FC. In this paper, we merged the hybrid water cycle moth-flame optimization (WCMFO) algorithm and the notion of measurement uncertainty to extract the parameters of the proton exchange membrane fuel cell (PEMFC) by using current-voltage characteristics (I-V). The integration of the notion of uncertainty made the proposed approach more robust to disturbance. Consequently, a curve (I-V) of the model estimated more precise and very similar to the real curve. To validate the performance of our approach, three commercial PEMFCs with their empirical data (I-V) are examined as Ballard, NedSstack PS6, and BCS 500-W. The problem of PEMFC models with seven parameters was investigated. The performance analysis is carried out by applying the sum of the squared error (SSE) and the root mean squared error (RMSE) to compare the calculated and empirical data, our approach is affirmed by its large superiority (SSE and RMSE are in the order of 10 −29 and 10 −15 , respectively) compared to other methods recently published in Literature (SSE and RMSE are in the order of 10 −2 and 10 −2 , respectively).

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Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer

Cited by 65 publications

References 30 publications

Parameters extraction of PEMFC's model using manta rays foraging optimizer

Parameters extraction of PEMFC's model using manta rays foraging optimizer

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

Parameters determination of proton exchange membrane fuel cell stack electrical model by employing the hybrid water cycle moth‐flame optimization algorithm

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