Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm

Singla, Manish Kumar; Nijhawan, Parag; Oberoi, Amandeep Singh

doi:10.1007/s11356-021-13097-0

Cited by 37 publications

(23 citation statements)

References 28 publications

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“…Consequently, BWO has been applied for the parameter estimation of two commercial PEMFCs, while the results are depicted in Table 7 in the twelfth row [132].…”

Section: Black Widow Optimizer (Bwo)mentioning

confidence: 99%

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

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“…Consequently, BWO has been applied for the parameter estimation of two commercial PEMFCs, while the results are depicted in Table 7 in the twelfth row [132].…”

Section: Black Widow Optimizer (Bwo)mentioning

confidence: 99%

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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“…iv) Fuel cell (FC) : Similar to battery, FC is an electro‐chemical device, which converts chemical energy to electrical energy 18,19 . The FC is offered in various configurations based on electrode types, operating characteristics, and power ranges.…”

Section: Description Of Systemmentioning

confidence: 99%

“…Their survival on the maternal web takes many days to a week, at which time, the presence of sibling cannibalism is observed. They then flee, aided by the storm 19 . After this, a chaotic theory is explained below.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…iv) Fuel cell (FC): Similar to battery, FC is an electrochemical device, which converts chemical energy to electrical energy. 18,19 The FC is offered in various configurations based on electrode types, operating characteristics, and power ranges. In the market, varieties of FCs are available but the polymer electrolyte membrane (PEM) is mostly used because of its excellent start up time and low operating temperature.…”

Section: Description Of Systemmentioning

confidence: 99%

“…They then flee, aided by the storm. 19 After this, a chaotic theory is explained below. Chaos is a deterministic and random phenomenon in a nonlinear system that is responsive to the initial conditions.…”

Section: Black Widow Optimization Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Cost–benefit comparison of fuel cell–based and battery‐based renewable energy systems

Singla

Nijhawan

Oberoi

2021

Intl J of Energy Research

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Summary Meeting the electrical demands of remote places by integrating different renewable energy technologies into a single hybrid system is quite demanding. The integration and optimal utilization of different accessible energy resources into a self‐contained micro‐grid are examined in this article. Using the Hybrid Optimization Model for Electric Renewable (HOMER) software (Version 3.14.0), an integrated hybrid renewable energy system (IHRES) strategy has been suggested and studied. This article is mainly focused on their levelized cost of energy (LCOE) supply and annualized cost system (ACS). Two combinations of energy sources and storage technologies, that is, i) solar photovoltaic (PV)/wind/battery and ii) solar PV/wind/fuel cell (FC) for the community load have been considered for the Kutch location in Gujarat. A recently proposed novel Hybrid Chaotic Particle Swarm Optimization and Black Widow Optimization (HCPSOBWO) method is used for optimum component sizing. To validate the effectiveness of the proposed approach, the results obtained are compared with HOMER software. The findings show that the proposed algorithm has excellent convergence characteristics and the capacity to provide high‐quality output. According to the simulation findings, the suggested off‐grid‐connected hybrid solar PV/wind/FC power system is the most suitable and cost‐effective option for the given location. The suggested system will pave the way for the creation of more sustainable, effective, and robust IHRES capable of meeting the energy needs of the whole mankind.

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Hybrid algorithm for parameter estimation of fuel cell

Singh

Nijhawan

Singla

et al. 2022

Intl J of Energy Research

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Summary Proton Exchange Membrane Fuel Cells (PEMFC) have been one of the most promising energy alternatives to the non‐renewable resources of energy in the last few years owing to their numerous advantages such as reliability and pollution‐free, sustainability, steady power generation, and non‐self‐discharging. Due to this, fuel cells have a wide range of applications in different areas. Therefore, it is very crucial to do an accurate and precise estimation of the parameters of PEMFC as modeling their characteristics has huge significance in the study, simulation, and development of highly efficient fuel cells. In this study, a new hybrid algorithm based on two widely used meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO) and Dingo Optimizer (DOX) is developed in order to estimate the parameters of PEMFC. First of all, the proposed algorithm is benchmarked on 10 functions in order to justify the algorithm. Further, the results of PEMFC parameter estimation obtained by the new proposed Hybrid Particle Swarm Optimization Dingo Optimizer (HPSODOX) algorithm are compared with other meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO), Dingo Optimizer (DOX), Grey Wolf Optimization (GWO) algorithm, and hybrid algorithms that is, Grey Wolf Optimization ‐Cuckoo Search (GWOCS), and PSOGWO. Then, the evaluation metric used in this manuscript is the Sum of Square Error (SSE). The new proposed hybrid algorithm performs better than other meta‐heuristic algorithms as it has the minimum value of SSE. For the datasheet of Ballard Mark V, the complete statistical error analysis and non‐parametric tests are carried out in order to evaluate the performance and superiority of the new proposed hybrid algorithm.

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Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm

Cited by 37 publications

References 28 publications

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Cost–benefit comparison of fuel cell–based and battery‐based renewable energy systems

Hybrid algorithm for parameter estimation of fuel cell

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