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

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). To optimize numerous hyperparameters of constructed data-driven model, a “discrete gradient-based optimization” method (DGO) is first proposed to achieve local-optimized hyperparameters as well as reduce the time complexity of grid searching. Based on the predicted parameters of PEMFCVs, a scheme is designed for fault prognosis and energy consumption prediction. All the procedure is trained and verified by real-world operation data to reflect the real-world applicable conditions for different seasons and vehicles. Results show that the proposed model can achieve accurate 30-time-step synchronous fuel cell temperature, hydrogen temperature, and hydrogen pressure prediction with mean-relative-errors (MREs) of 0.54%, 0.85% and 0.71%; The fault can be accurately prognosed five minutes ahead with MRE of 0.71% to provide driver sufficient time to deal with the fault; The small MRE of 6.3% for energy consumption prediction also indicates the effectiveness of proposed model. The proposed whole method can be applied in the real-world PEMFCVs for long-time-step parameter prediction, fault prognosis, and energy consumption prediction. The proposed DGO can be used to optimize other data-driven methods.