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AI-based optimization of PEM fuel cell catalyst layers for maximum power density via data-driven surrogate modeling

journal contribution
posted on 04.05.2020 by Bowen Wang, Biao Xie, Jin Xuan, Kui Jiao
© 2020 Elsevier Ltd Catalyst layer (CL) is the core electrochemical reaction region of proton exchange membrane fuel cells (PEMFCs). Its composition directly determines PEMFC output performance. Existing experimental or modeling methods are still insufficient on the deep optimization of CL composition. This work develops a novel artificial intelligence (AI) framework combining a data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variables global optimization for improving the maximum power density of PEMFCs. Simulation results of a three-dimensional computational fluid dynamics (CFD) PEMFC model coupled with the CL agglomerate model constitutes the database, which is then used to train the data-driven surrogate model based on Support Vector Machine (SVM), a typical AI algorithm. Prediction performance shows that the squared correlation coefficient (R-square) and mean percentage error in the test set are 0.9908 and 3.3375%, respectively. The surrogate model has demonstrated comparable accuracy to the physical model, but with much greater computation-resource efficiency: the calculation of one polarization curve will be within one second by the surrogate model, while it may cost hundreds of processor-hours by the physical CFD model. The surrogate model is then fed into a Genetic Algorithm (GA) to obtain the optimal solution of CL composition. For verification, the optimal CL composition is returned to the physical model, and the percentage error between the surrogate model predicted and physical model simulated maximum power densities under the optimal CL composition is only 1.3950%. The results indicate that the proposed framework can guide the multi-variables optimization of complex systems.

Funding

National Natural Science Foundation of China (grant No. 51861130359)

UK Royal Society (grant No. NAF\R1\180146)

National Natural Science Foundation of Tianjin (China) for Distinguished Young Scholars (Grant No. 18JCJQJC46700).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Energy Conversion and Management

Volume

205

Pages

112460

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Energy Conversion and Management and the definitive published version is available at https://doi.org/10.1016/j.enconman.2019.112460

Acceptance date

31/12/2020

Publication date

2020-01-08

Copyright date

2020

ISSN

0196-8904

eISSN

1879-2227

Language

en

Depositor

Prof Jin Xuan (email: J.Xuan@lboro.ac.uk | regno: 5032143). Deposit date: 4 May 2020

Article number

112460

Exports