Optimization of high-temperature proton exchange membrane fuel cell flow channel based on genetic algorithm

被引:32
作者
Huang, Taiming [1 ]
Wang, Wei [1 ]
Yuan, Yao [1 ]
Huang, Jie [1 ]
Chen, Xi [1 ]
Zhang, Jing [1 ]
Kong, Xiangzhong [1 ]
Zhang, Yan [1 ]
Wan, Zhongmin [1 ]
机构
[1] Hunan Inst Sci & Technol, Coll Mech Engn, Yueyang 414006, Peoples R China
基金
中国国家自然科学基金;
关键词
HT-PEMFC; Genetic algorithm; Flow field; Channel optimization; NEURAL-NETWORK; MODEL; PERFORMANCE; DESIGN; PEMFC; FIELD; IDENTIFICATION; DURABILITY; PARAMETER;
D O I
10.1016/j.egyr.2021.02.062
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The performance of high-temperature polybenzimidazole (PBI) proton exchange membrane fuel cells (HT-PEMFCs) doped with phosphoric acid is strongly influenced by the configuration of its flow channel. In this research, genetic algorithm (GA) is adopted to optimize original three-dimensional simulation model by obtaining the optimal design of the channel configuration. Set the widths of top and bottom edges of the anode/cathode flow channels as independent variables with constrained range to optimize HT-PEMFC performance. The ratio of the pressure drop loss to the output power of the HT-PEMFC is set as the objective function. The results show that the widths of the top and bottom edges are 0.513 mm 0.635 mm at the anode side, and 0.752 mm and 1.159 mm at the cathode side, respectively. The shape of the cross-sectional of the flow channel is trapezoidal, which can achieve the best performance. In the anode side, the narrow contact surface between the flow channel and the GDL can increase the diffusion rate of the hydrogen. For cathode, the larger contact surface formed by the flow channel and the GDL can provide more gas to carry out the electrochemical reaction. The pressure drop loss and output power of the optimal model are 1.7% and 6.5% greater than those of the original model at 0.4 V, respectively. The results can contribute to improve the design and operation of HT-PEMFCs in the future. (C) 2021 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:1374 / 1384
页数:11
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