耐久性
计算机科学
航空航天
燃料电池
工艺工程
电化学能量转换
化学能
汽车工程
机械工程
材料科学
电化学
航空航天工程
化学
工程类
化学工程
数据库
物理化学
有机化学
电极
作者
Wuyi Ming,Peiyan Sun,Zhen Zhang,Wenzhe Qiu,Jinguang Du,Xiaoke Li,Yanming Zhang,Guojun Zhang,Kun Liu,Yu Wang,Xudong Guo
标识
DOI:10.1016/j.ijhydene.2022.10.261
摘要
A fuel cell is a power generation device that directly converts chemical energy into electrical energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle, and small-scale stationary engine applications. The complex phenomena including mass/heat transfer, electrochemical reactions, and ion/electron conduction, can significantly affect the energy efficiency and durability of fuel cells, but are difficult to determine completely. Machine learning (ML) performs well in solving complex problems in engineering applications and scientific research. In this paper, a systematic review is conducted to explore ML methods, including traditional ML and deep learning (DL) methods, applied to fuel cells for performance evaluation (material selection, chemical reaction modeling, and polarization curves), durability prediction (state of health, fault diagnostics, and remaining useful life), and application monitoring. Then comparisons of traditional ML and DL methods are discussed, while the similarities and differences between ML and integrated physics simulations are also concluded. Eventually, the scope of ML methods applied to fuel cells is presented, and outlooks of future researches on ML applications in fuel cells are identified.
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