No abstract

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

No abstract

No abstract

Based on a coupled finite element method (FEM) and computational fluid dynamics (CFD) model, the structural deformation and performance of a proton exchange membrane fuel cell (PEMFC) under different membrane water contents are studied. The water absorption behavior of the membrane is investigated experimentally to obtain its expansion coefficient with water content, and the Young’s modulus of the membrane and catalyst (CL) are obtained through a tensile experiment. The simulation results show that the deformation of the membrane increases with water content, and membrane swelling under the channel is larger than that under the rib, forming a surface bump under the channel. The structural changes caused by the membrane water content have little effect on the performance of PEMFC in the low-current density range; while its influence is significant in the medium- and high-current density range. A medium membrane water content value of 12 achieves the best fuel cell performance due to the balance of membrane resistance and mass transport.