Application of Machine Learning in Fuel Cell Research

Danqi Su, Jiayang Zheng, Junjie Ma, Zizhe Dong, Zhangjie Chen and Yanzhou Qin ()
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Danqi Su: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Jiayang Zheng: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Junjie Ma: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Zizhe Dong: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Zhangjie Chen: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Yanzhou Qin: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China

Energies, 2023, vol. 16, issue 11, 1-32

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.

Keywords: fuel cells; machine learning; neural networks; support vector machines; random forests (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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