Image Recognition of Gas Diffusion Layer Structural Features Based on
Artificial Intelligence 2022-01-7040
Gas diffusion layer (GDL), as a critical constituent of the proton exchange
membrane fuel cell (PEMFC), plays a key role in mass, heat, electron, and
species transport. GDL generally has two distinct layers: a macro-porous
substrate (MPS) and a micro-porous layer (MPL). The fibers in MPS and the cracks
formed during the deposition process on the surface of MPL change the overall
transport capacity and effect the output performance of PEMFC. In this paper,
methods of identifying the structural features of fibers and cracks in GDL
images based on artificial intelligence are proposed. The block probabilistic
Hough transform and the quadric voting based on the weighted K-means algorithm
are programmed to realize the fiber feature extraction, and the crack feature
extraction is realized by the regional connectivity algorithm and the geometric
feature calculation based on the circumscribed graph of the crack region.
Besides, the scanning electron microscope (SEM) images of GDL are analyzed to
validate the feasibility and accuracy of the algorithm. Results can prove that
the fiber feature recognition accuracy can reach more than 90% and the usage of
various characteristic parameters to quantify the crack is necessary. The image
processing technology based on artificial intelligence can capture the
microstructural features of GDL images and extract feature parameters, which
provides a reliable tool for GDL image analysis and has guiding significance for
further research on GDL.
Citation: Lan, S., Lin, R., Lou, M., and Lu, K., "Image Recognition of Gas Diffusion Layer Structural Features Based on Artificial Intelligence," SAE Technical Paper 2022-01-7040, 2022, https://doi.org/10.4271/2022-01-7040. Download Citation
Author(s):
Shunbo Lan, Rui Lin, Mingyu Lou, Kai Lu
Affiliated:
Tongji University
Pages: 8
Event:
SAE 2022 Vehicle Electrification and Powertrain Diversification Technology Forum
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Fuel cells
Microscopy
Artificial intelligence (AI)
Materials properties
SAE MOBILUS
Subscribers can view annotate, and download all of SAE's content.
Learn More »