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

Qun Niu, Letian Zhang, Kang Li

Research output: Contribution to journalArticlepeer-review

150 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)1173-1185
Number of pages13
JournalEnergy Conversion and Management
Volume86
Early online date23 Jul 2014
DOIs
Publication statusPublished - Oct 2014

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