Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

Balasubramanian, Karthik; Jacob, Basil; Priya, K.; Sangeetha, K.; Rajasekar, N.; Babu, T. Narendiranath

doi:10.1016/j.egypro.2015.07.244

Cited by 31 publications

(10 citation statements)

References 9 publications

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“…It should be noted, that although Table contains 45 references, there are still a few more publications on the topic which the authors of this review were unable to access. In addition, some studies handle the parameter estimation problem from a different perspective, either utilizing simulated data , more detailed , or simpler model structures, or in conjunction with simulation studies . These contributions are therefore excluded from the review tables.…”

Section: Review Of Parameter Optimization For the Electrochemical Modelmentioning

confidence: 99%

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Ohenoja

Leiviskä

2020

Fuel Cells

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The optimal operation of fuel cells in changing environmental and variable load conditions requires mathematical modeling. The electrochemical behavior of polymer electrolyte membrane fuel cells (PEMFC) is commonly described with a semi-empirical model requiring fuel cell specific model parameter values. A large number of different nature inspired, heuristic optimization methods have been proposed for this PEMFC parameter estimation problem. In this study, those studies are listed and critically reviewed. In particular, the aim is to elaborate the generalization ability of the results and discuss the fair comparison of the algorithms used for the parameter estimation of the polarization curve. The observations made in this review could further increase the quality of future contributions in this particular area, as well as applications of heuristic optimization methods in other related problems in fuel cell systems.

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Section: Review Of Parameter Optimization For the Electrochemical Modelmentioning

confidence: 99%

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Ohenoja

Leiviskä

2020

Fuel Cells

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“…Genetic algorithm is a stochastic optimization technique, which is inspired under the natural selection laws and genetics. John Holland [35][36][37][38] particles. Fitness amounts most be evaluated for all of the iterations of particles, and the best particle is founded.…”

Section: 4optimizationmentioning

confidence: 99%

Thermodynamic performance analysis and optimization of a power and hydrogen generation system based on geothermal flash, dual-pressure organic Rankine cycle using zeotropic mixtures, and PEM electrolyzer

Talesh¹

2020

Preprint

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In this study, a thermodynamic model and optimization of a flash-binary geothermal system are proposed for power and hydrogen production. The binary cycles contain a dual-pressure ORC cycle and proton exchange membrane electrolyzer for power and hydrogen production. The combination of dual-pressure organic Rankine cycle with zeotropic mixtures enhances the performance of the system considerably, and selection of the best kind of zeotropic mixture as the working fluids of the DORC unit is one of the important methods to improve the performance of the suggested system. Also, the Genetic algorithm is employed to optimize the net output power of the system, in which the results of optimization show that the best performance belongs to Pentane (0.467)/Butane (0.533) with first and second-law efficiencies of 16.66%, 58.03%, respectively, and hydrogen production of 0.3683 kg/hr. Besides, for the base case simulation, the energy and exergy efficiencies of the whole system are reported by about 16.66% and 58.03%, respectively, also for this condition, the derived power from the proposed model and overall exergy destruction is calculated approximately 132.41 kW, and 90.08 kW, respectively and geothermal heat source generates 0.306 kg/hr hydrogen by the proton exchange membrane electrolyzer.

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“…Metaheuristic algorithms are global optimization techniques which do not impose any restrictions on the problem formulation and have the ability to solve various complex problems [11]. In the literature, many metaheuristic algorithms have been suggested for extracting the parameters of PV solar cell models, such as the Genetic Algorithm (GA) [12,13], the Cuckoo Search (CS) Algorithm [11,14], Particle Swarm Optimization (PSO) [15][16][17], Differential Evolution (DE) algorithm [18], Artificial Bee Colony (ABC) algorithm [19][20][21], Artificial Algorithm of Bee Swarm Optimization (ABSO) [22], Bacterial Foraging Optimization (BFO) algorithm [23,24], Biogeography-Based Optimization (BBO) algorithm [25], Floral Pollination Algorithm (FPA) [26,27], Jaya Optimization Algorithm (JAYA) [28,29], Salp Swarm Algorithm (SSA) [30], Bird Mating Optimization (BMO) algorithm [31], Teaching-Learning-Based Algorithm (TLBO) [20,[32][33][34], Whale Optimization Algorithm (WOA) [35][36][37], Backtracking Search Algorithm (BSA) [38], Sine-Cosine Algorithm (SCA) [39], Imperialist Competitive Algorithm (ICA) [40,41], Multiverse Optimizer (MVO) algorithm [42], Ant-Lion Optimizer (ALO) algorithm [43,44], Eagle Strategy (ES) [45], Cat Swarm Optimization (CSO) [46], Harmony Search (HS)…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm

Nicaire

Steve

Salomé

et al. 2021

International Journal of Photoenergy

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The global demand for renewable energy is growing, and one of the proposed solutions to this energy crisis is the use of photovoltaic systems. So far, they are a reliable solution, as they are nonpolluting and can be used almost anywhere on the planet. However, the design and development of more efficient photovoltaic cells and modules require an accurate extraction of their intrinsic parameters. Up to date, metaheuristic algorithms have proven to be the best methods to obtain accurate values of these intrinsic parameters. Hence, to extract these parameters reliably and accurately, this paper presents an optimization method based on the principle of bald eagle search (BES) during fish hunting. This search is divided into three steps: in the first stage (space selection), the eagle selects the space with the largest number of prey; in the second stage (space search), the eagle moves into the selected space to search for prey; in the third stage (dive), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Thus, we used the proposed BES algorithm to determine the parameters of the single-diode model (SDM), the double-diode model (DDM), and the PV modules. This algorithm converges very quickly and gives a root mean square error (RMSE) of 9.8602 e − 04 for the single-diode model and 9.8248 e − 4 for the dual-diode model. The results obtained show that the proposed algorithm is more efficient than the other methods available in the literature, in terms of the better accuracy of the results obtained. The good harmony of the I-V and P-V characteristic curve of the calculated parameters with that of the measured data from a PV module/cell data sheet proves that the proposed BES should be used among the methods provided in the literature for the identification of PV solar cell parameters.

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Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction

Cited by 31 publications

References 9 publications

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Thermodynamic performance analysis and optimization of a power and hydrogen generation system based on geothermal flash, dual-pressure organic Rankine cycle using zeotropic mixtures, and PEM electrolyzer

Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm

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