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

Jiaping Xie, Hao Yuan (), Yufeng Wu, Chao Wang, Xuezhe Wei and Haifeng Dai
Additional contact information
Jiaping Xie: School of Automotive Studies, Tongji University, Shanghai 201804, China
Hao Yuan: School of Automotive Studies, Tongji University, Shanghai 201804, China
Yufeng Wu: School of Automotive Studies, Tongji University, Shanghai 201804, China
Chao Wang: School of Automotive Studies, Tongji University, Shanghai 201804, China
Xuezhe Wei: School of Automotive Studies, Tongji University, Shanghai 201804, China
Haifeng Dai: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2023, vol. 16, issue 14, 1-18

Abstract: Electrochemical impedance is a powerful technique for elucidating the multi-scale polarization process of the proton exchange membrane (PEM) fuel cell from a frequency domain perspective. It is advantageous to acquire frequency impedance depicting dynamic losses from signals measured by the vehicular sensor without resorting to costly impedance measurement devices. Based on this, the impedance data can be leveraged to assess the fuel cell’s internal state and optimize system control. In this paper, a residual network (ResNet) with strong feature extraction capabilities is applied, for the first time, to estimate characteristic frequency impedance based on eight measurable signals of the vehicle fuel cell system. Specifically, the 2500 Hz high-frequency impedance (HFR) representing proton transfer loss and 10 Hz low-frequency impedance (LFR) representing charge transfer loss are selected. Based on the established dataset, the mean absolute percentage errors (MAPEs) of HFR and LFR of ResNet are 0.802% and 1.386%, respectively, representing a superior performance to other commonly used regression and deep learning models. Furthermore, the proposed framework is validated under different noise levels, and the findings demonstrate that ResNet can attain HFR and LFR estimation with MAPEs of 0.911% and 1.610%, respectively, even in 40 dB of noise interference. Finally, the impact of varying operating conditions on impedance estimation is examined.

Keywords: proton exchange membrane fuel cell; electrochemical impedance; impedance estimation; residual network (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:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/14/5556/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/14/5556/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:14:p:5556-:d:1200271

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().