Parameter optimization for tubular solid oxide fuel cell stack based on the dynamic model and an improved genetic algorithm

Yang, Jie; Li, Xi; Jiang, Jian; Li, Jian; Zhao, Lei; Jiang, Jin; Wu, Xiao; Xu, Lin

doi:10.1016/j.ijhydene.2011.02.019

Cited by 48 publications

(11 citation statements)

References 19 publications

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“…Based on the Butler‐Volmer equation, 45 the activation voltage (𝑉 𝑎𝑐𝑡 ) can be calculated as follows:

V_{act} goodbreak= \frac{2 italicRT}{zF} \sinh^{- 1} ((), \frac{1}{2 I_{0}}) goodbreak= normalA \sinh^{- 1} ((), \frac{1}{2 I_{0}})

where z presents the number of moles of electrons and I 0 denotes the density of the exchange current, its value can be defined as follows 24 :

I_{0} goodbreak= k_{1} T e^{\frac{- k_{1}}{T}}

Where k 1 and k 2 are empirical factors used for calculating apparent exchange current.…”

Section: Sofc Modelingmentioning

confidence: 99%

“…The interest in the identification approach based metaheuristic algorithms (MAs) has been raised significantly in the last years. In Reference 24, a modified GA has been used to identify the parameters of the SOFC stack model. The adaptive differential evolution (DE) algorithm has been used in Reference 25.…”

Section: Introductionmentioning

confidence: 99%

“…where z presents the number of moles of electrons and I 0 denotes the density of the exchange current, its value can be defined as follows 24 :…”

Section: Introductionmentioning

confidence: 99%

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Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

Rezk

Ferahtia

Sayed

et al. 2022

Intl J of Energy Research

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Summary Enhancing the solid oxide fuel cell (SOFC) is an attractive topic for academic research. One of the investigated solutions is to enhance the modeling accuracy. This paper presents a robust strategy based on a bald eagle search optimization (BES) algorithm to determine the best parameters of the solid oxide fuel cell (SOFC) model. Six unknown parameters to be determined are E, A, Io, RΩ, B, and Imax. The sum of mean squared error (SMSE) between the measured and estimated stack voltages is minimized. Two scenarios of the SOFC operation mode are considered. The first is a steady‐state model, and the second is the transient/dynamic‐state model. In a dynamic state‐based model, a demand disturbance is implemented, and the performance of the constructed model is examined. Furthermore, a comparison with previously reported approaches and other programmed algorithms has been performed to approve the proposed strategy. The high performance of the BES algorithm in terms of accuracy and robustness makes it an excellent algorithm for this application. To approve the performance of the proposed strategy, it will be investigated for several operating temperatures with multiple algorithms. The relevant results are auspicious where the final fitness, which expresses the identification accuracy, has been minimized by the BES to 2.6906 × 10−6 for 1073 K, 6.3942 × 10−6 for 1213 K 1.0401 for 1273 K.

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“…Based on the Butler‐Volmer equation, 45 the activation voltage (𝑉 𝑎𝑐𝑡 ) can be calculated as follows:

V_{act} goodbreak= \frac{2 italicRT}{zF} \sinh^{- 1} ((), \frac{1}{2 I_{0}}) goodbreak= normalA \sinh^{- 1} ((), \frac{1}{2 I_{0}})

where z presents the number of moles of electrons and I 0 denotes the density of the exchange current, its value can be defined as follows 24 :

I_{0} goodbreak= k_{1} T e^{\frac{- k_{1}}{T}}

Where k 1 and k 2 are empirical factors used for calculating apparent exchange current.…”

Section: Sofc Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

Rezk

Ferahtia

Sayed

et al. 2022

Intl J of Energy Research

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show abstract

“…Chakraborty [154] used a genetic programming approach in an SOFC system. Yang et al [160] presented an improved genetic algorithm to optimize the parameters in the simple SOFC electrochemical model. The results of the optimization parameters showed that the improved genetic algorithm had more precision and stability compared to its simple genetic counterpart.…”

Section: Optimization Of Sofc Operational Parametersmentioning

confidence: 99%

Simulation and Sensitivity Analysis for Various Geometries and Optimization of Solid Oxide Fuel Cells: A Review

Tonekabonimoghaddam

Shamiri²

2021

Eng

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Solid oxide fuel cells (SOFCs) are considered as one of the most promising fuel cell types for application as high efficiency power generators. This work reviews the use of computational fluid dynamics (CFD) to maximise SOFC performance and life, and minimise cost, by considering numerous configurations and designs. A critical analysis of available literature proves that detailed research on the simulation of thermal stress and its damaging impact on the SOFC is still in its early stage of development. Numerical simulation is expected to help optimize the design, operating parameters and fuel cell materials. Therefore, sensitivity analysis of fuel cell parameters using simulation models is analysed to address the issue. Finally, the present status of the SOFC optimization efforts is summarized so that unresolved problems can be identified and solved.

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“…15 On the other hand, many works have been developed to identify the optimal parameters of SOFC model. For instance, Yang et al 16 introduced a modified genetic algorithm based on the calibrated fitness value to extract the optimal parameters of SOFC based-equivalent circuit. Yousri et al 1 proposed an approach based on multiswarm marine predators' algorithm (MSMPA) to get the optimal equivalent circuit parameters of the SOFC model.…”

Section: Introductionmentioning

confidence: 99%

Model parameters extraction of solid oxide fuel cells based on semi‐empirical and memory‐based chameleon swarm algorithm

Rizk‐Allah

El‐Hameed

El‐Fergany

2021

Intl J of Energy Research

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Parameter optimization for tubular solid oxide fuel cell stack based on the dynamic model and an improved genetic algorithm

Cited by 48 publications

References 19 publications

Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

Simulation and Sensitivity Analysis for Various Geometries and Optimization of Solid Oxide Fuel Cells: A Review

Model parameters extraction of solid oxide fuel cells based on semi‐empirical and memory‐based chameleon swarm algorithm

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