A Hybrid Differential Evolution for Optimum Modeling of PEM Fuel Cells

Zhu, Weijie; Fang, Jian‐an; Zhang, Wenbing; Xu, Yuan; Tong, Le

doi:10.1007/s13369-014-0958-1

Cited by 6 publications

(4 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For finding global/near-global solutions with high computational efficiency, traditional and adaptive differential evolution (DE) optimization algorithms were also used in many PEMFC parameter extraction studies. 3,[27][28][29][30][31][32] However, the convergence speed toward finding the global/ near-global solution is relatively lower than PSO-based algorithms. 5 Another set of low complexity level algorithms, as biogeography-based optimization (BBO), 33 bioinspired P-based systems optimization algorithm, 34 simulated annealing (SA), 35 and teaching learning-based optimization algorithm (TLBO), 7,36 were also used in PEMFC parameter extraction studies; however, these low complexity level algorithms might fall in local optima.…”

Section: Introductionmentioning

confidence: 99%

“…Despite better performance was achieved using PSO‐based algorithms, the level of complexity of the problem increased. For finding global/near‐global solutions with high computational efficiency, traditional and adaptive differential evolution (DE) optimization algorithms were also used in many PEMFC parameter extraction studies 3,27‐32 . However, the convergence speed toward finding the global/near‐global solution is relatively lower than PSO‐based algorithms 5 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm

2020

View full text Add to dashboard Cite

Among the various fuel cell types, proton exchange membrane fuel cells (PEMFCs) have prominent characteristics that make them unique in applications. However, the preciseness of results of a PEMFC model depends on the availability of the parameters, which are missing in the datasheets provided by the manufacturers and vendors, and this explains why it becomes convenient to estimate such parameters for a complete and precise PEMFC model that closely matches the experimental measures under different operation conditions. In this work, a novel solution methodology based on applying an ensemble sinusoidal parameter adaptation incorporated with L-SHADE, called LSHADE-EpSin optimization algorithm, is proposed to solve the PEMFC parameter extraction problem. The proposed methodology is applied to four commercial PEMFCs: 250 W PEMFC; NedStack PS6, 6 kW; BCS 500 W; and SR-12500 W, and the results obtained are compared with the results obtained by using other recent optimization algorithms. Furthermore, several statistical tests were performed to validate the proposed model's performance and compare between the investigated algorithms. The results show the effectiveness of the approach proposed using the LSHADE-EpSin algorithm in estimating the optimal PEMFC parameters under different operating conditions compared to the other optimizers for the four studied stacks.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm

2020

View full text Add to dashboard Cite

show abstract

“…4 Therefore, recent heuristic-based optimization methods have been used to overcome these shortfalls and easily resolve these problems. 23 A DE convergence trend is enhanced by using two neighborhood search, dynamic crossover probability, and integrated scaling factor. Simple GA has been employed to define PEMFC parameters.…”

Section: Introductionmentioning

confidence: 99%

“…21,22 In addition, a Markovian switching system is used to regulate DE population size and distribution. 23 A DE convergence trend is enhanced by using two neighborhood search, dynamic crossover probability, and integrated scaling factor. 24 Moreover, flower pollination algorithm has been adopted to estimate unknown PEMFC parameters.…”

Section: Introductionmentioning

confidence: 99%

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

View full text Add to dashboard Cite

Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

show abstract

The effects of under-ribs convection on enhanced drainage parallel flow field for proton exchange membrane fuel cell

Yang

Zhan

et al. 2022

Korean J. Chem. Eng.

View full text Add to dashboard Cite

A Hybrid Differential Evolution for Optimum Modeling of PEM Fuel Cells

Cited by 6 publications

References 44 publications

A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm

A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

The effects of under-ribs convection on enhanced drainage parallel flow field for proton exchange membrane fuel cell

Contact Info

Product

Resources

About