Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network

Guo, Chengjun; Lu, Juncheng; Zhong, Tian; Guo, Wei; Darvishan, Aida

doi:10.1016/j.enconman.2018.12.088

Cited by 127 publications

(66 citation statements)

References 41 publications

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“…A closer look to the above-mentioned (1) to (10), seven parameters such as (ξ 1 , ξ 2 , ξ 3 , ξ 4 , λ, R c , &β) are required to be well-defined optimally. 6,8,15,25,26 In a common practice, these uncertain parameters are not given in the PEMFCs builder's datasheet and should be identified carefully for the sake of accurate modelling of PEMFCs stacks.…”

Section: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

“…The preciseness of the model depends mainly on minimizing the error value between the simulation and experimental results. The rigorous PEMFC model helping in saving a huge effort exerted and long‐time taken to achieve good results . There are many types of models representing the PEMFC.…”

Section: Introductionmentioning

confidence: 99%

“…Added to the aforesaid, number of heuristic‐based approaches, namely SFLA, firefly, and colonial competitive methods, backtracking framework, teaching‐learning, modified particle swarm optimization, multi‐verse optimizer, GHO, VSA and DE, and flower pollination for the PEMFC's model calibration and precise representation are reported in the published literature.…”

Section: Introductionmentioning

confidence: 99%

“…The rigorous PEMFC model helping in saving a huge effort exerted and long-time taken to achieve good results. 15 There are many types of models representing the PEMFC. Several empirical methods are utilized in modeling this PEMFC.…”

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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: Mathematical Representation Of Pemfcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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

2020

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“…• Modeling, control, and optimization of the FC system to improve the fuel economy [10][11][12]; • Developing innovative solutions and advanced technologies to improve the lifetime, reliability and safety in operation of the FC system [13,14]; • Numerical models for the control of hydrogen and thermal/electric energies productions through Solid Oxide Electrolyzer/Fuel Cells [15]: • Proposal of innovative stand-alone or grid-connected RES HPS architectures, which can be optimized based on advanced Energy Management Strategies (EMSs) [16][17][18] and Global Maximum Power Point Tracking (GMPPT) control algorithms [19][20][21] applied to available RESs (photovoltaic systems, wind turbines etc.) in order to optimally ensure the power flow balance on the DC bus (and/or the AC bus) [22][23][24] and improve the harvested energy from the RESs [25][26][27]; • Hybridization of the RES HPS with an FC system as backup energy source (FC/RES HPS) to mitigate the RES power variability and load dynamics by controlling the generated FC power at the level of the required power on the DC bus [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus

2019

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In this paper, a systematic analysis of seven control topologies is performed, based on three possible control variables of the power generated by the Fuel Cell (FC) system: the reference input of the controller for the FC boost converter, and the two reference inputs used by the air regulator and the fuel regulator. The FC system will generate power based on the Required-Power-Following (RPF) control mode in order to ensure the load demand, operating as the main energy source in an FC hybrid power system. The FC system will operate as a backup energy source in an FC renewable Hybrid Power System (by ensuring the lack of power on the DC bus, which is given by the load power minus the renewable power). Thus, power requested from the batteries’ stack will be almost zero during operation of the FC hybrid power system based on RPF-control mode. If the FC hybrid power system operates with a variable load demand, then the lack or excess of power on the DC bus will be dynamically ensured by the hybrid battery/ultracapacitor energy storage system for a safe transition of the FC system under the RPF-control mode. The RPF-control mode will ensure a fair comparison of the seven control topologies based on the same optimization function to improve the fuel savings. The main objective of this paper is to compare the fuel economy obtained by using each strategy under different load cycles in order to identify which is the best strategy operating across entire loading or the best switching strategy using two strategies: one strategy for high load and the other on the rest of the load range. Based on the preliminary results, the fuel consumption using these best strategies can be reduced by more than 15%, compared to commercial strategies.

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Importance and applications of DOE/optimization methods in PEM fuel cells: A review

Karanfil

2019

Int J Energy Res

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Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network

Cited by 127 publications

References 41 publications

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

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

Optimization of the Fuel Cell Renewable Hybrid Power System Using the Control Mode of the Required Load Power on the DC Bus

Importance and applications of DOE/optimization methods in PEM fuel cells: A review

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