An LSTM and ANN Fusion Dynamic Model of a Proton Exchange Membrane Fuel Cell

Abstract

Proton exchange membrane fuel cell (PEMFC) has great application prospects due to its low emission and high efficiency. An accurate model to predict the dynamic output voltage is essential for the optimal control of PEMFC for the applications on vehicles and power stations. In this paper, a novel deep learning framework with the Long short-term memory (LSTM) and ANN Fusion is proposed to develop the PEMFC dynamic model by extracting both the historical and current information. The LSTM extracts the temporal information from the past PEMFC states with its order determined with autocorrelation function (ACF) and partial autocorrelation (PACF), while the influence of the system current inputs is learnt by the ANN. Then the outputs of LSTM and ANN are concatenated with the multiple information fused to predict the PEMFC dynamic output voltage. After validated by the operating data from a lab-scale PEMFC system, the LSTM and ANN Fusion model is compared with existing models, such as SVR, ANN and LSTM methods. The comparison results show that the proposed LSTM and ANN Fusion model can provide the best prediction performance with the lowest Mean Square Error (MSE) of 1.303. The proposed LSTM and ANN Fusion model can be helpful to develop the optimal control strategy of PEMFC.