A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Wang, Chu; Li, Zhongliang; Outbib, Rachid; Dou, Manfeng; Zhao, Dongdong

doi:10.1016/j.ijhydene.2022.01.121

Cited by 42 publications

(19 citation statements)

References 32 publications

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“…In particular, methods in long short-term memory networks (LSTM) framework have demonstrated their strong performance in short-term SoH prediction [14,15,16,22,37]. However, LSTM performance becomes unsatisfactory with increased prediction horizon length which is observed and indicated in our previous work [15,23,36]. Interestingly, the cause of this issue may stem from the powerful "memory" ability of the LSTM, which incorrectly records irrelevant features in the training set.…”

Section: Introductionmentioning

confidence: 80%

A fusion prognostics strategy for fuel cells operating under dynamic conditions

Wang

Dou

et al. 2022

eTransportation

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Section: Introductionmentioning

confidence: 80%

A fusion prognostics strategy for fuel cells operating under dynamic conditions

Wang

Dou

et al. 2022

eTransportation

Self Cite

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“…Since the degradation mechanism of fuel cell has not been fully discovered, it is hard to describe the degradation process accurately with models [14,15]. Data-driven approaches [16][17][18][19][20][21][22][23][24][25][26][27][28], on the contrary, can make predictions without fully understanding the degradation mechanisms as long as sufficient data are available. Therefore data-driven approaches have received wide attention [26].…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven approaches [16][17][18][19][20][21][22][23][24][25][26][27][28], on the contrary, can make predictions without fully understanding the degradation mechanisms as long as sufficient data are available. Therefore data-driven approaches have received wide attention [26]. Silva et al [16] proposed an adaptive neurofuzzy inference system (ANFIS) based on time series.…”

Section: Introductionmentioning

confidence: 99%

“…In the long-term prediction, a variable-step strategy was proposed, but it requires short-term prediction for correction. Wang et al [26] used a navigation sequence to drive the LSTM network to improve the prediction performance of LSTM models and overcome the error accumulation in longterm prediction. However, the data-driven approach also has drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…However, the distribution of confidence intervals of the DGP method is far beyond satisfaction when the training samples are not sufficient. Considering the powerful nonlinear time series prediction ability of LSTM [26,32], combining the LSTM network and GPR model to form the LSTM-GPR model with a depth structure is expected to achieve accurate degradation voltage prediction and reliable confidence interval prediction. For long-term forecasting, model-based approaches, such as EKF [9], have a lower forecasting accuracy due to the absence of measurement data as observations during the forecasting process.…”

Section: Introductionmentioning

confidence: 99%

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A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression

2022

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An accurate prediction of the remaining useful life (RUL) of a proton exchange membrane fuel cell (PEMFC) is of great significance for its large-scale commercialization and life extension. This paper aims to develop a PEMFC degradation prediction method that incorporates short-term and long-term predictions. In the short-term prediction, a long short-term memory (LSTM) neural network is combined with a Gaussian process regression (GPR) probabilistic model to form a hybrid LSTM-GPR model with a deep structure. The model not only can accurately forecast the nonlinear details of PEMFC degradation but also provide a reliable confidence interval for the prediction results. The results showed that the proposed LSTM-GPR model outperforms the single models in both prediction accuracy and confidence interval. For the long-term prediction, a novel RUL prediction model based on an extended Kalman filter (EKF) and GPR is proposed. The GPR model is used to solve the problem that the EKF cannot update the model parameters in the prediction stage. The results showed that the proposed EKF-GPR model can achieve better RUL prediction than the model-based approach and the data-driven approach.

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Trend‐focused dynamic degradation prediction based on echo state networks in automotive fuel cells

Yue,

Zhang,

Teng

et al. 2024

Energy Science & Engineering

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Fuel cell technology is a promising alternative to traditional internal combustion engines in various applications, especially in transportation applications. However, the high cost and limited lifetime of fuel cells have hindered their widespread commercialization. Accurately predicting fuel cell lifetime is crucial for reducing the cost of ownership, ensuring safety, and promoting the adoption of this technology. The objective of the present work is to develop a tool that is able to estimate the lifespan of a proton exchange membrane fuel cell and to predict its behavior to anticipate failures. Therefore, this paper contributes to proposing a multi‐input time‐series prediction network based on an echo state network, which takes the future current into consideration. A degradation trend extraction method is proposed in this paper and the remaining useful life of the fuel cell is predicted. Results have shown that the proposed methods in both short‐term and long‐term prediction have achieved satisfying prediction accuracy.

show abstract

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Cited by 42 publications

References 32 publications

A fusion prognostics strategy for fuel cells operating under dynamic conditions

A fusion prognostics strategy for fuel cells operating under dynamic conditions

A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression

Trend‐focused dynamic degradation prediction based on echo state networks in automotive fuel cells

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