RETRACTED: New optimized technique for unknown parameters selection of SOFC using Converged Grass Fibrous Root Optimization Algorithm

Shi, Hong; Li, Jing; Zafetti, Nicholas

doi:10.1016/j.egyr.2020.05.024

Cited by 27 publications

(5 citation statements)

References 35 publications

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“…Until now, multitudinous advanced meta-heuristic algorithms have been developed astonishingly to identify unknown parameters in precise SOFC systems. The converged grass fibrous root optimization algorithm (CGROA) (Shi et al, 2020) is a novel optimized technique that is utilized to select unknown parameters in the electrochemical model of the SOFC, where the convergence speed and statistical analysis are applied to present a clearer contrast result. In the study by Wei and Stanford, (2019), an optimized algorithm based on the chaotic binary shark smell optimization (CBSSO) algorithm is recommended, which alleviates the limitations of the optimization process and obtains satisfactory unknown parameter results, upon which superior performance in global search is fully verified.…”

Section: Methods Of Parameter Identificationmentioning

confidence: 99%

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

2022

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Section: Methods Of Parameter Identificationmentioning

confidence: 99%

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

2022

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“…Converged grass fibrous root is a method presented by Shi et al [14] for determining the optimum outcomes of the SOFC model. Seven parameters are assigned as decision variables during the optimization process: the conventional potential, the prevailing limitation density, the Tafel line slope, a constant that is affected by the SOFC's operational state, the area-specific resistance, the anode transfer, and the cathode exchange current density.…”

Section: Parminder Singhmentioning

confidence: 99%

Parameter Identification of a Solid Oxide Fuel Cell Model under Different Temperature Conditions

Singh

Sandhu

2023

Chem Eng & Technol

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A new effective method for optimization of unknown parameters in solid oxide fuel cell (SOFC) stack models is suggested. The overall voltage of the SOFC stack depends on these predicted parameters. The goal is to reduce the mean square error (MSE) between the empirical and the predicted polarization curve obtained using the method. Black widow optimization (BWO), a metaheuristic method inspired by nature, describes the minimization process. This algorithm is made to alter the search space, avoid local optima, and deliver greater efficiency in the exploitation and exploration stages. Situations based on multidimensional benchmark functions and SOFC stack temperature variations are investigated to ascertain the system consistency.

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“…Among these methods, the metaheuristic optimization-based methodologies were superior in resolving the SOFC parameter estimation problem due to their reliability, robustness, and simplicity. Shi et al [19] proposed a strategy, Converged Grass Fibrous Root, to determine the best parameters of the SOFC model. Both temperature and pressure variation are considered.…”

Section: Introductionmentioning

confidence: 99%

Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization

et al. 2021

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An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10−6 at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC.

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RETRACTED: New optimized technique for unknown parameters selection of SOFC using Converged Grass Fibrous Root Optimization Algorithm

Cited by 27 publications

References 35 publications

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

Parameter Identification for Solid Oxide Fuel Cell Models: Crucial Comments

Parameter Identification of a Solid Oxide Fuel Cell Model under Different Temperature Conditions

Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization

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