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Temperature and humidity cycling during frequent startup-shutdown of proton exchange membrane (PEM) fuel cell (PEMFC) can lead to crack formation in the catalyst layer (CL) or even at the interface between the CL and PEM, decreasing the effective activation area and degrading the fuel cell performance. This study develops a two-dimensional PEMFC model using COMSOL software which is well validated, and investigates the effect of different lengths and positions of CL/PEM interface crack (delamination) on the mass transport and performance of PEMFC. The results show that both the crack length and position have significant influence on the cell performance, especially at high current density. The presence of crack leads to an uneven distribution of cell current density, and the performance of the cell decreases with the increase of the crack length due to decreased activation area. The crack near the inlet has the greatest adverse impact on the cell performance, compared to other positions near the outlet or in the middle of the rib. Compared to the cell without cracks, the decrement in current density is 5.05%, 9.55% and 15.52%, respectively, for the crack in the middle of the rib, near the outlet and near the inlet. [ABSTRACT FROM AUTHOR]

Rational design of flow channel can promote the removal of liquid water and effectively improve the performance of proton exchange membrane fuel cell (PEMFC). Compared with the conventional parallel straight channels, the block flow channel has a better water removal effect and is more beneficial to the fuel cell performance. In this study, a random forest-trained surrogate model is combined with a genetic algorithm to achieve optimization of the block structure applied in a novel two-block flow channel. A three-dimensional multiphase model based on the volume of fluid (VOF) method is employed to obtain the data sets by varying the block structure (length, height, and width) which are used to train a random forest-based data-driven surrogate model for predicting the water removal time (T) and the maximum pressure drop (∆P) of the channel. Then, the data-driven surrogate model is integrated into the genetic algorithm to optimize the design of the block. The results show that the output values of the surrogate model trained by random forest fit well with the actual values of the physical model, indicated by small mean square error (MSE) of T and ∆P which are 0.1191 and 0.0209, respectively. After optimization by the genetic algorithm, the block parameters of 0.6 mm in length, 0.375 mm in width, and 0.75 mm in height are found to be optimal, and the results are validated by the physical model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]