Optimization of Linear Loading Strategy for Fuel Cell Initiation Based on Gaussian Process Regression Model - Jianbin SU; Shaofang LIN; Yuanxing SHEN; Lei SHI; Chenxi QIU (Electrochemistry, 2024)
This paper developed a one-dimensional multiphase model to analyze the cold start behavior of fuel cell, focusing on the effects of various parameters such as loading rates, maximum loading current densities, and coolant flow rates. The findings reveal that the loading rate significantly influences the ice volume fraction and the duration required for cold start, while exerting a lesser effect on voltage and the voltage variation. Cold start fail at maximum loading current densities below 0.4 A cm−2 due to a heating rate insufficient to outpace the rate of ice formation. However, as the maximum loading current density surpasses this threshold, successful start-ups are achieved, although the benefits to voltage, start-up time, and voltage variation diminish with increasing current density. The coolant flow rate primarily impacts the temperature distribution’s uniformity across individual cells, with negligible effects on overall cold start performance. Utilizing Gaussian process regression models for training and prediction reveals that elevating the loading rate and maximum loading current density can effectively mitigate ice formation. The relationship between these variables and performance metrics such as voltage, start-up time (The time required from the loading start to the successful start-up), and voltage variation is characterized by an initial increase followed by a subsequent decrease as the loading rate climbs. Conversely, the influence of the maximum loading current density on these metrics is comparatively modest.