Application of Machine Learning in Fuel Cell Research

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.

Impedance Acquisition of Proton Exchange Membrane Fuel Cell Using Deeper Learning Network

“…The proposed scheme is validated and compared to other frequently used regression and deep learning models under a series of test sequences with different noise levels and input signals. The main conclusions are as follows: (1) The ResNet can achieve a high estimation accuracy for HFR and LFR under different test sequences. It has an overall mean absolute percentage error (MAPE) of 0.802% and 1.386%, respectively, which outperforms other regression and deep learning models.…”

“…The development of new energy vehicles has an influential impact on global energy security and environmental protection, with fuel cell vehicles being a potential contender for the next generation due to their zero emissions, quick hydrogenation time, and extended driving range. Of the various fuel cells, the proton exchange membrane (PEM) fuel cell is largely utilized in the field of transportation because of its high energy density, rapid dynamic response, and high conversion efficiency [1]. However, the large-scale commercial application of the PEM fuel cell is still impeded by its durability.…”

Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients

“…In recent years, machine learning methods have become increasingly popular in various fields. In fuel cell research, machine learning algorithms can be successfully used for performance prediction, lifetime prediction, and fault diagnosis of fuel cells, with good accuracy in solving nonlinear problems ( Su et al, 2023 ); machine learning has become one of the most promising research methods in novel material screening and material performance prediction ( Huang et al, 2023 ); and in healthcare, machine learning has been used to examine health-related data, and medical professionals can enhance diagnosis and treatment through machine learning applications ( An et al, 2023 ).…”

Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization

“…ML greatly improves work efficiency and accuracy. Driven by increasing computer power and algorithmic advances, ML has become a powerful tool for finding data patterns 24 , and it is widely applied in various fields, such as biology 25 , 26 , medicine 27 , 28 , healthcare 29 , environment 30 , fuel cells 31 and energy 32 . ML methods can also be used to develop new AMPs.…”