Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor

El-Hay, Enas A.; El‐Hameed, Mohamed A.; El‐Fergany, Attia A.

doi:10.1016/j.energy.2018.08.104

Cited by 30 publications

(11 citation statements)

References 39 publications

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“…46 The MRFO can solve multimodal single objective engineering optimization problems. Similar to other challenging methodologies, the initial step of the MRFO is the random initialization process as expressed in Equation (11).…”

Section: Mrfomentioning

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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Section: Mrfomentioning

confidence: 99%

“…There are many applications of the PEMFC in industrial applications. The PEMFC is applied to improve the performance of switched reluctance motor drives application in . Moreover, it is extensively applied to the distribution networks .…”

Section: Introductionmentioning

confidence: 99%

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

2020

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“…10,34,[46][47][48] The maximum thermal current of this system is 225 A. 10,34,[46][47][48] The maximum thermal current of this system is 225 A.…”

Section: Case Study (3): 6 Kw Nedstack Ps6 Pemfcs Stackmentioning

confidence: 99%

“…In this last study, Nedstack PS6 PEMFCs stack (in which, 65 cells are linked together in series) with the rated active power of 6 kW and its main characteristics and operating data can be found in the literatures. 10,34,[46][47][48] The maximum thermal current of this system is 225 A. The same procedures performed for the above presented two case studies are followed.…”

Section: Case Study (3): 6 Kw Nedstack Ps6 Pemfcs Stackmentioning

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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“…However, the additional components result in higher cost. The dragonfly algorithm of switched reluctance motor is introduced to anticipate optimal operations of autonomous stacked proton membrane fuel cells serving a switched reluctance motor [14]. The emphasis is dragonfly algorithm with six controlling parameters, e.g., fuel cell temperature, air flow rate, air pressure, fuel pressure, and turn on/off angles of switched reluctance motor.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Novel Converter Topology for Photovoltaic Pumps Based on Switched Reluctance Motor

Zan

et al. 2019

Energies

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In order to improve the performance of switched reluctance motor (SRM) systems for photovoltaic (PV) pumps, this paper introduces a new converter topology for SRM with controllable multiple power sources. Only simple switching components need to be added at the front end of the asymmetric half-bridge converter in this topology, which enables the control of multiple power sources. The new PV pump system has four operating modes, which are the PV panel driven mode, battery bank driven mode, dual-source driven mode, and battery charging mode. By adjusting the state of the front-end converter switch, the voltage tracking of PV panel can be achieved, providing a stable bus voltage for the SRM system. By controlling the battery bypass switch, the bus voltage of SRM system can be increased, thereby increasing the system power level. Simulations and experiments based on a four-phase 8/6 SRM demonstrate the effect of the novel converter proposed in this paper.

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Performance enhancement of autonomous system comprising proton exchange membrane fuel cells and switched reluctance motor

Cited by 30 publications

References 39 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

Design and Analysis of a Novel Converter Topology for Photovoltaic Pumps Based on Switched Reluctance Motor

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