Steady-State Model Enabled Dynamic PEMFC Performance Degradation Prediction via Recurrent Neural Network

Qiang Liu, Weihong Zang, Wentao Zhang, Yang Zhang, Yuqi Tong and Yanbiao Feng ()
Additional contact information
Qiang Liu: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Weihong Zang: State-Assigned Electric Vehicle Power Battery Testing Center, China North Vehicle Research Institute, Beijing 100072, China
Wentao Zhang: State-Assigned Electric Vehicle Power Battery Testing Center, China North Vehicle Research Institute, Beijing 100072, China
Yang Zhang: State-Assigned Electric Vehicle Power Battery Testing Center, China North Vehicle Research Institute, Beijing 100072, China
Yuqi Tong: State-Assigned Electric Vehicle Power Battery Testing Center, China North Vehicle Research Institute, Beijing 100072, China
Yanbiao Feng: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Energies, 2025, vol. 18, issue 10, 1-20

Abstract: Proton exchange membrane fuel cells (PEMFC), distinguished by rapid refueling capability and zero tailpipe emissions, have emerged as a transformative energy conversion technology for automotive applications. Nevertheless, their widespread commercialization remains constrained by technical limitations mainly in operational longevity. Precise prognostics of performance degradation could enable real-time optimization of operation, thereby extending service life. This investigation proposes a hybrid prognostic framework integrating steady-state modeling with dynamic condition. First, a refined semi-empirical steady-state model was developed. Model parameters’ identification was achieved using grey wolf optimizer. Subsequently, dynamic durability testing data underwent systematic preprocessing through a correlation-based screening protocol. The processed dataset, comprising model-calculated reference outputs under dynamic conditions synchronized with filtered operational parameters, served as inputs for a recurrent neural network (RNN). Comparative analysis of multiple RNN variants revealed that the hybrid methodology achieved superior prediction fidelity, demonstrating a root mean square error of 0.6228%. Notably, the integration of steady-state physics could reduce the RNN structural complexity while maintaining equivalent prediction accuracy. This model-informed data fusion approach establishes a novel paradigm for PEMFC lifetime assessment. The proposed methodology provides automakers with a computationally efficient framework for durability prediction and control optimization in vehicular fuel cell systems.

Keywords: PEM fuel cell; performance degradation; model-involved neural network; precise semi-empirical model; long-short term memory; gate recurrent unit (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/1996-1073/18/10/2665/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/10/2665/ (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:10:p:2665-:d:1661126

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 ().