A review on lifetime prediction of proton exchange membrane fuel cells system

/pdf/a-novel-long-short-term-memory-networks-based-data-driven-11mgttew.pdf

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

A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell

/pdf/symbolic-deep-learning-based-prognostics-for-dynamic-39drzwfb.pdf

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

/pdf/a-fusion-prognostics-strategy-for-fuel-cells-operating-under-2br0cusq.pdf

A fusion prognostics strategy for fuel cells operating under dynamic conditions

The prognostic of proton exchange membrane fuel cells (PEMFCs) degradation and the estimation of its remaining useful life (RUL) are effective ways to improve the reliability of the target system and reduce maintenance costs, which is of great significance for the wide commercialization of PEMFCs. Many factors cause the degradation of PEMFCs, and these factors are often difficult to measure accurately. The prognostic method based on long short-term memory networks (LSTMs) has better memory ability for time series and has been demonstrated able to describe the degradation trend of PEMFCs. However, the traditional LSTM prediction algorithm seems to easily fall into the local optimal solution in long-term prediction cases. Overfitting like errors may result in an imprecise or even unstable prognostic. This paper proposes a novel method, named navigation sequence driven LSTMs (NSD-LSTMs), to enhance the accuracy of PEMFCs degradation trend prediction. Two types of PEMFCs aging test data under different load conditions were used to verify the performance of NSD-LSTMs. Experimental results show that, compared with traditional LSTMs, NSD-LSTMs can improve the accuracy of trend prediction. Accurate degradation prognostic can be used to predict RUL and provide guidance for the commercial application of PEMFCs.

/pdf/proton-exchange-membrane-fuel-cells-prognostic-strategy-4god3pwl12.pdf

Proton Exchange Membrane Fuel Cells Prognostic Strategy Based on Navigation Sequence Driven Long Short-term Memory Networks

/pdf/data-driven-prognostics-based-on-time-frequency-analysis-and-d9yt3cdh.pdf

Chu Wang

Papers

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

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

A fusion prognostics strategy for fuel cells operating under dynamic conditions

Proton Exchange Membrane Fuel Cells Prognostic Strategy Based on Navigation Sequence Driven Long Short-term Memory Networks

Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load