A novel degradation model of proton exchange membrane fuel cells for state of health estimation and prognostics. (3rd September 2021)

• Record Type: Journal Article
• Authors: Wang, Penghao; Liu, Hao; Chen, Jian; Qin, Xiaoping; Lehnert, Werner; Shao, Zhigang; Li, Ruiyu
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 61(2021)
• Page Start: 31353
• Page End: 31361
• Publication Date: 2021-09-03
• Subjects: Proton exchange membrane fuel cells -- Degradation model -- State of health -- Remaining useful life -- Prognostics; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2021.07.004
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The major challenges for the commercialization of proton exchange membrane fuel cells (PEMFCs) are durability and costs. Prognostics and health management technology is helpful to extend the lifetime and reduce the maintenance costs of PEMFCs. However, the common degradation model, especially in the model-based method and the hybrid method, has the disadvantages of low generality and accuracy. A novel degradation model is proposed by introducing a polarization resistance in this paper to overcome the above disadvantages. Combining the novel degradation model and particle filter, a model-based method is proposed to estimate the state of health (SOH) and predict the future degradation trend (FDT) and the remaining useful life (RUL) of PEMFCs. Then, two actual degradation datasets of PEMFCs are used to validate the model, the RUL errors of the novel degradation model on these two datasets are 12.6% and 12.7%, respectively, while the errors of the common degradation model are 17.8% and 33.4%, respectively. The results prove that the proposed degradation model has higher generality and accuracy than the common model. Highlights: A novel degradation model is established by introducing a polarization resistance. The proposed model-based method can accurately estimate SOH, FDT and RUL. Two sets of experimental data are used to validate the prognostics method. The time delay caused by noise reduction is solved by the delay compensation method.