A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

[Display omitted] •Solar cell and PEM fuel cell parameter estimations are investigated in the paper.•A new biogeography-based method (BBO-M) is proposed for cell parameter estimations.•In BBO-M, two mutation operators are designed to enhance optimization performance.•BBO-M provides a competitive alt...

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Vydáno v:Energy conversion and management Ročník 86; s. 1173 - 1185
Hlavní autoři: Niu, Qun, Zhang, Letian, Li, Kang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.10.2014
Elsevier
Témata:
Algorithms
Applied sciences
Biogeography-based optimization
chaos theory
Energy
Energy. Thermal use of fuels
Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc
Exact sciences and technology
Fuel cells
Mathematical analysis
Mathematical models
mutation
Mutation strategy
Mutations
Natural energy
Optimization
Parameter estimation
PEM fuel cell
Photovoltaic conversion
Solar cell
solar cells
Solar cells. Photoelectrochemical cells
Solar energy
Strategy
Solar cell
Parameter estimation
Mutation strategy
Biogeography-based optimization
PEM fuel cell
Models
Algorithm
Modeling
Optimization
Fuel cell
ISSN:0196-8904, 1879-2227
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Shrnutí:[Display omitted] •Solar cell and PEM fuel cell parameter estimations are investigated in the paper.•A new biogeography-based method (BBO-M) is proposed for cell parameter estimations.•In BBO-M, two mutation operators are designed to enhance optimization performance.•BBO-M provides a competitive alternative in cell parameter estimation problems. Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2014.06.026