Abstract:

The low efficiency， low accuracy and strict experimental requirements for the detection of the proton exchange membrane fuel cell （PEMFC） catalyst layer porous structure can’t adapt to the increasingly large-scale industry development system. Therefore， to address the problem， this paper innovatively proposes the genetic algorithm-particle swarm optimization-Otsu （GA-PSO-Otsu） algorithm to realize efficient， accurate and self-adaptive identification of pore size distribution and porosity calculation of the scanning electron microscope （SEM） of the catalyst layer. Firstly， Gaussian convolution and binary threshold are combined to maximize the inter-class variance between the foreground and background to effectively reducethe impact of noise and manual adjustment of parameters on accuracy and efficiency， which ensures automatic noise reduction and pore structure detection. Furthermore， the genetic algorithm based particle swarm optimization method is proposed to solve the problem of long time consuming caused by traverse parameters and to avoidlocal optimization， with the advantages of high accuracy and high efficiency. Lastly， the comparative analysis of different algorithms applied on various PEMFC catalyst SEMs with different component ratios and gray scales indicate that the proposed method has high robustness， self-adaptiveness and practicability. Compared with the traditional Otsu approach which traverses all parameters， the porosity error of the proposed method is less than 0.5% and the calculation time is significantly reduced by 26.2%.

Key words: proton exchange membrane fuel cell （PEMFC）, catalyst layer pore size distribution, catalyst layer porosity, genetic algorithm （GA）, particle swarm optimization （PSO）, Otsu