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Bi-subgroup optimization algorithm for parameter estimation of a PEMFC model
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DOI：10.1016/j.eswa.2022.116646.png)
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Abstract
En 中文
Proton exchange membrane fuel cell (PEMFC) has the advantages of cleanliness, environmental protection, and high stability, and is widely used in the fields of astronautics, military, and so on. How to extract the PEMFC model parameters more precisely is a key problem, which can be transformed into a highly nonlinear optimization problem. Therefore, an efficient optimization algorithm is needed. Swarm intelligence algorithms are used for solving various complex optimization problems. In this paper, a bi-subgroup optimization algorithm (BSOA) is proposed. The BSOA divides the population into two different sub-populations, one of which is to develop more optimal solutions and the other is to mine more optimal solutions within the population. Hence, it can overcomes the drawback that a single population search tends to fall into local optimal solutions and enhances the diversity of the population. By constructing and analyzing the Markov model, it is proved that the BSOA is a global convergence algorithm. Finally, the BSOA is applied to the estimation of the unknown parameters of the PEMFC modules and compared with the state-of-the -art algorithms. The simulation results show that BSOA is an effective algorithm for estimating the unknown parameters of the PEMFC model.
Keywords:
Parameter estimation
Swarm intelligence
Convergence analysis
Bi-subgroup optimization
PEMFC model
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7.5
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3.0W
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10.2W
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