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

Mohamed, Fahim; El‐Fergany, Attia A.; Hasanien, Hany M.

doi:10.1002/er.4809

Cited by 73 publications

(41 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To signify the results of MRFO, comparisons to other methods in the literatures such as NNA, SSA, GWO, and GHO are made as revealed in Table . It is made known that the proposed MRFO has the finest SSD value ever as per tabulated values.…”

Section: Simulation Results and Performance Assessmentsmentioning

confidence: 99%

“…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%

“…This fitness value is minimized by the MRFO to ensure good matching among theoretical and actual measurements. The SSD is itemized in Equation (19) which is regularly used in literatures . The same is used for fair evaluations to other optimizers' results.…”

Section: Procedures Of Mrfo and Problem Statementmentioning

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%

See 4 more Smart Citations

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

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Simulation Results and Performance Assessmentsmentioning

confidence: 99%

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: Procedures Of Mrfo and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2020

Self Cite

View full text Add to dashboard Cite

show abstract

Parameter Estimation of Fuel Cell Using Chaotic Mayflies Optimization Algorithm

Gupta

Nijhawan

Ganguli

2021

Advcd Theory and Sims

View full text Add to dashboard Cite

Proton exchange membrane fuel cells (PEMFCs) have been known to be a feasible method for sustainable power generation to meet the ever-increasing electricity demand with no greenhouse gases emission. Thus, under varying operating circumstances, the PEMFCs have gained considerable importance as an alternative energy source. The optimal operation of PEMFC needs to be ensured to exploit its advantages to the maximum limit. In this direction, there is an extensive need for parameter extraction of PEMFC. Many researchers have applied evolutionary optimization approaches to estimate the PEMFCs parameters as the precise modeling of these cells are not possible. To optimize unknown parameters of PEMFCs, a metaheuristic algorithm is proposed, that is, chaotic mayfly algorithm (CMA) is being proposed in this manuscript. The model's statistical effects are more aligned with the actual experimental findings, according to an experimental finding. The results so obtained suggest that CMA variants are a valuable and efficient method of estimating the parameters of PEMFCs models. Even the nonparametric tests like the Friedman ranking test, Wilcoxon's rank-sum test, and Mood's median test suggest that the proposed algorithm shows better accuracy of the extracted parameters as compared to the other algorithms namely considered in this work.

show abstract