Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization

Xiangdong Wang, Zerong Huang, Daxing Zhang, Haoyu Yuan, Bingzi Cai, Hanlin Liu, Chunsheng Wang, Yuan Cao, Xinyao Zhou and Yaolin Dong ()
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Xiangdong Wang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Zerong Huang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Daxing Zhang: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Haoyu Yuan: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Bingzi Cai: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Hanlin Liu: Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, China
Chunsheng Wang: School of Automation, Central South University, Changsha 410083, China
Yuan Cao: School of Automation, Central South University, Changsha 410083, China
Xinyao Zhou: School of Automation, Central South University, Changsha 410083, China
Yaolin Dong: School of Automation, Central South University, Changsha 410083, China

Energies, 2024, vol. 17, issue 23, 1-13

Abstract: This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these limitations, this study proposes a data-driven approach based on the gated recurrent unit (GRU) neural network, optimized by the grey wolf optimizer (GWO). The integration of the GWO automates the hyperparameter tuning process, enhancing the predictive performance of the GRU network. The proposed GWO-GRU method was validated utilizing actual PEMFC data under dynamic load conditions. The results demonstrate that the GWO-GRU method achieves superior accuracy compared to other standard methods. The method offers a practical solution for online PEMFC degradation prediction, providing stable and accurate forecasting for PEMFC systems in dynamic environments.

Keywords: proton-exchange membrane fuel cells; degradation prediction; durability test; gated recurrent unit; grey wolf optimizer; accuracy; complexity (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: 2024
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