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- Title
State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network.
- Authors
Huo, Haibo; Chen, Jiajie; Wang, Ke; Wang, Fang; Jin, Guangzhe; Chen, Fengxiang
- Abstract
Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC.
- Subjects
PROTON exchange membrane fuel cells; BATTERY management systems; FAULT diagnosis; SERVICE life
- Publication
Sustainability (2071-1050), 2023, Vol 15, Issue 11, p9094
- ISSN
2071-1050
- Publication type
Article
- DOI
10.3390/su15119094