Prognostics for remaining useful life estimation in proton exchange membrane fuel cell by dynamic recurrent neural networks. (November 2022)

• Record Type: Journal Article
• Authors: Chang, Te-Jen; Cheng, Shan-Jen; Hsu, Chang-Hung; Miao, Jr-Ming; Chen, Shih-Feng
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 9441
• Page End: 9452
• Publication Date: 2022-11
• Subjects: Prognostics -- Proton exchange membrane fuel cell -- Dynamic recurrent neural networks -- Nonlinear autoregressive neural network -- Nonlinear autoregressive with exogenous inputs neural network; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.07.032
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: This paper proposes a nonlinear dynamic recurrent neural network (DRNN) prognostics method for predicting the performance degradation trend and estimating the remaining useful life (RUL) of a proton exchange membrane fuel cell (PEMFC). The conducted DRNN prognostic methods are based on a nonlinear autoregressive neural network (NARNN) model and nonlinear autoregressive with exogenous inputs neural network (NARXNN) model, with the goal of taking actions to extend the life of the PEMFC. To effectively remove data noise or spikes and provide smooth reconstruction, a locally weighted regression (LWR) method is used for pre-processing. The DRNN model parameters for the time lags are processed by an autocorrelation function (ACF). For comparing the predictions within the different DRNN models, the data sets are divided into three different prognostic configurations for predicting and estimating the remaining life of the PEMFC. The results show that the NARXNN model has the best predictions for the degradation trend and accurate remaining useful life, i.e. better than the NARNN model. Finally, the prognostic capability of the proposed DRNN method is compared with those in the literature.