###### Abstract  
The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate. [PERSON] et al. [7] combined the three models and Kalman filtering, respectively, and found that the exponential model performed the best with the maximum error only 2.3% under different conditions. [PERSON] et al. [8] pointed out that, if the operating conditions were not constant, neither voltage nor state of health (SOH) alone could predict the RUL accurately. Therefore, models were built depending on voltage and SOH firstly, and then the final model was obtained by assigning weights and bias correction to the two initial models. [PERSON] et al. [9] established the degradation model of fuel cells by combining wavelet analysis, extreme learning machine and genetic algorithm. Firstly, wavelet analysis was used to decompose the voltage aging waveform into several sub-waveforms. Then, the extreme learning machine was applied to establish a degradation model for each sub-waveform. Finally, the overall model was optimized through genetic algorithm. The MAE of this model was less than 3%. Moreover, the second-order Gaussian model was established to estimate SOH and RUL [10]. Data-driven approaches are widely used. The Echo State Network (ESN) was used to predict the RUL of the fuel cells in both static environment and dynamic environments [11; 12], and adaptive neuro-fuzzy inference systems (ANFISs) were put forward to forecast the degradation of fuel cells under constant load current [13; 14]. [PERSON] et al. [15] predicted the RUL based on the sparse autoencoder (SAE) and deep neural network (DNN). The SAE is applied to extract the prediction features automatically, and the DNN is applied to realize the RUL prediction. This method is superior to K-nearest neighbor (KNN) and SVR. A feedforward neural network was used to forecast the voltage and the outlet temperature, and the prediction errors are around 9.4% and 5.6% [16; 17]. Moreover, methods such as least-squares SVM (LSSVM) [18], relevance vector machine (RVM) [19], the grey model [20], the summation Wavelet and extreme learning machine (SW-ELM) [21] have been applied in this field. With the development of deep learning, the recurrent neural network (RNN) models are applied to the RUL prediction of hydrogen fuel cells more frequently. [PERSON] et al. [22] used the long short-term memory (LSTM) network to predict the RUL, and the accuracy of this method was 99.23%. Furthermore, bidirectional handshaking LSTM was applied to RUL prediction when there were only short series of observations [23]. [PERSON] et al. [24] proposed a stacked LSTM (S-LSTM) model for predicting the RUL. Hybrid methods combine the data-driven methods and model-driven methods. An extended Kalman filter method is proposed to solve the problem [25]. This method estimates the time-varying parameters of the fuel cells aging model.