Exploring Performance Degradation of Proton Exchange Membrane Fuel Cells Based on Diffusion Transformer Model

Lingling Lv, Pucheng Pei (), Peng Ren (), He Wang and Geng Wang
Additional contact information
Lingling Lv: China National Institute of Standardization, Beijing 100191, China
Pucheng Pei: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Peng Ren: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
He Wang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Geng Wang: China National Institute of Standardization, Beijing 100191, China

Energies, 2025, vol. 18, issue 5, 1-22

Abstract: Proton exchange membrane fuel cells (PEMFCs) stand at the forefront of energy conversion technology, efficiently converting the chemical energy of hydrogen and oxygen directly into electricity. Research on predicting the remaining useful life of PEMFCs has long been a focus, as it plays a crucial role in preventing failures and mitigating safety risks. This paper introduces a robust diffusion transformer (DiT) model, which is a novel approach leveraging generative artificial intelligence (GAI) technology to innovate the existing methods for predicting the performance degradation of PEMFCs. This model employs random Gaussian noise to generate stable performance degradation data of PEMFCs under specified conditions. The predictive accuracy is then assessed by benchmarking against a bi-directional long short-term memory recurrent neural network (Bi-LSTM) using two distinct experimental datasets. The evaluation shows that the DiT model achieves higher predictive accuracy than the reference model. Specifically, the mean absolute prediction error is reduced by 72.7% under steady-state conditions and 59.3% under dynamic conditions. Correspondingly, the remaining useful life error (RE) is diminished by 80% and 88%, respectively. These findings indicate that the DiT model has significant potential in PEMFCs performance degradation research.

Keywords: proton exchange membrane fuel cell; performance degradation prediction; diffusion model; transformer model; generative artificial intelligence (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: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/5/1191/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/5/1191/ (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:18:y:2025:i:5:p:1191-:d:1602354

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 ().