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Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction by Da Li; Zhaosheng Zhang; Litao Zhou; Peng Liu; Zhenpo Wang; Junjun Deng is a Engineering article available to read on EtoBox.

What is Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction about?

Proton exchange membrane fuel cell vehicle (PEMFCV) is considered to be a promising way to cope with the environment pollution and energy exhaustion. But PEMFCV is also suffering from some difficulties for the real-world application, including different kinds of faults and hydrogen refueling. This raises requirement for timely and accurate fault prognosis and energy consumption prediction. However, limited and sparse parameters obtained by onboard sensors and random influencing factors during the real-world vehicular operation make the PEMFCV hardly be modelled. To cope with the issue, this study firstly puts forward a vehicle state-driving behavior factor (VDF) construction of PEMFCV to consider as comprehensive factors as possible and uses maximal information coefficient (MIC) to extract related factors for model training. Then a data-driven model is constructed to achieve synchronous multi-time-step and multi-parameter prediction for the fuel cell and hydrogen system in real-world PEMFCVs. The model considers noise denoising, spatial feature processing and temporal feature processing by combining convolutional neural network (CNN) and gated recurrent unit neural networks (GRU).

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Author

Da Li; Zhaosheng Zhang; Litao Zhou; Peng Liu; Zhenpo Wang; Junjun Deng

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Engineering (Physical Sciences)