Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

Yang, Zixuan; Liu, Qian; Zhang, Leiyu; Dai, Jialei; Razmjooy, Navid

doi:10.1016/j.energy.2020.118738

Cited by 88 publications

(19 citation statements)

References 33 publications

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“…Moreover, the PEMFC's model is high nonlinear, multi-variant and its variables are strongly coupled and severely affected by the operating conditions. Hence, the dynamics and accurate modelling have become more complicated and time-consuming when employed by such conventional techniques [36][37][38][39][40][41][42]. Therefore, there is an imperative demand to derive robust and feasible techniques to tackle such issue.…”

Section: C) Power Rating (Kw) Meritsmentioning

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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Section: C) Power Rating (Kw) Meritsmentioning

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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“…e metaheuristic algorithms are derived from various phenomena from nature, human society, animal hunting behavior, and so forth. Several algorithms have been proposed in this field [26][27][28][29], for example, World Cup Optimizer [30], Ant Lion Optimizer (ALO) [31], Chimp Optimization Algorithm [32], Harris Hawks Optimization [33], and mayfly optimization algorithm [34]. In the present research, a novel modified model of the ermal Exchange Optimization (TEO) algorithm has been presented to achieve optimal results for the considered methodology [5].…”

Section: The Developed Thermal Exchange Optimization Algorithmmentioning

confidence: 99%

Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Shi

Hamian

2021

Computational Intelligence and Neuroscience

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Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.

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“…of high-efficiency fuel cell systems [4,5]. But PEMFC modelling is complicated by itself because of its non-linear, multivariate, and strongly coupled characteristics.…”

Section: Pemfc Modellingmentioning

confidence: 99%

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Kumar

Gnanasekar

Andrews

et al. 2021

IET Renewable Power Gen

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Proton exchange membrane fuel cell (PEMFC) is widely popular for its inherent advantages like low operating temperature, high efficiency, durability, and reliability. However, modelling its characteristics is often restricted by virtue of its shortage of data and strongly coupled multivariate behaviour. At the same time, when operated within its temperature limits, PEMFC systems exhibit maximum efficiency; otherwise, they cause membrane dryness, poor ionic conductivity and less efficiency. Therefore, PEMFC modelling alongside thermal management is of interest in recent years. With the benefits rendered by the metaheuristics algorithm, an integrated approach is emphasised in this work for achieving twin objectives of modelling PEMFC together with thermal management. An aspiration to enhance PEMFC performance via mathematical modelling, flower pollination algorithm for parameter identification as well as thermal conductivity property computation is outlined. Simulated values are experimentally matched using copper-based nanofluids. The selection of copper (Cu) is justified by its thermal conductivity property and cost. For brevity, synthesis procedure, characterisation and estimation of thermal conductivity property is expounded in methodology. Numerous experimentations on various vol% fractions combinations lying between 0.05% and 0.5% indicate that Cu nanoparticle when dispersed in base fluids such as water and ethylene glycol (EG) substantially improves thermal conductivity property. Especially, among different combinations, nanofluid with EG and water mixture achieves the highest thermal conductivity value of 1.4255. 1Perhaps, fuel cell modelling is extremely helpful in performance prediction, simulation analysis, design, and developmentThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

Cited by 88 publications

References 33 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

Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

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