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 ()
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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
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