## 4 Results and Discussion  
The information of the experimental equipment is as follows: the processor of the computer is Intel i7-10875H @ 2.30 Ghz, memory is 16 GB, and the programming environment is Python 3.7.0. The configurations of the GRU model and dataset are as follows: the optimizer is Adam, the loss function is MAE, the output layer is 1, the number of neurons  
Figure 3: Cell structure of GRU.  
in the hidden layer is 50, the epoch size is 200, the batch size is 60, the starting point of prediction is 650 and the forecast result is shown in Figure 4.  
As shown in Figure 4, the initial voltage of the hydrogen fuel cell is 3.33 V when the voltage drops below 95% (voltage threshold is 3.1635 V) or 96% (voltage threshold is 3.1968 V) for the first time after the starting point of prediction; thus, the fuel cell is considered failed. The results are as follows: RMSE (Root Mean Square Error) is 0.0026, MAPE (Mean Absolute Percentage Error) is 0.0675, R2 (Coefficient of Determination) is 0.9891, EOPs (Ends of Prediction) are 69 h (95% voltage threshold) and 272 h (96% voltage threshold), and EOLs (Ends of Life) are 86 and 271 h. Compared with the BPNN model, the prediction accuracy of the GRU model is obviously improved. The RMSE of GRU is reduced to 12.56% of BPNN, and the MAPE of GRU is reduced to 14.89% of BPNN.  
Compared with BPNN, the accuracy of prediction when GRU is applied is much higher; the difference between BPNN and GRU is shown in Table 2.  
Moreover, Figure 5 shows that GRU has a fast convergence rate; this model converges from the 55 th epoch roughly.  
At the same time, the comparison of these three neural networks with a prediction starting of 650 is shown in Figure 6a.  
The comparison of these three neural networks is shown in Figure 6b for when the prediction starting point changes (e.g., to 850 h), and the differences of evaluation indexes are shown in Table 3.  
As can be seen from Figure 6 and Table 3, the LSTM and the GRU perform better than BPNN. The prediction results of the LSTM and the GRU networks are similar, and they  
Figure 6: Comparisons of prediction effect between BPNN, LSTM and GRU: (**a**) starting point is 650 h and (**b**) starting point is 850 h.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Starting** & \multirow{2}{*}{**RMSE**} & **MAPE** & \multirow{2}{*}{**R2**} & **Convergence** \\  & **Point** & & & & **Point** \\ \hline \multirow{2}{*}{BPNN} & 650 & 0.0207 & 0.4533 & 0.3061 & 65 \\  & 850 & 0.0038 & 0.1050 & 0.9765 & 40 \\ \hline \multirow{2}{*}{LSTM} & 650 & 0.0033 & 0.0831 & 0.9827 & 90 \\  & 850 & 0.0016 & 0.0432 & 0.9958 & 70 \\ \hline \multirow{2}{*}{GRU} & 650 & 0.0026 & 0.0675 & 0.9891 & 80 \\  & 850 & 0.0027 & 0.0724 & 0.9877 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different neural networks and prediction starting points.  
are suitable for dealing with the long time-series problem. However, the structure of the GRU network is simpler, and the convergence speed is faster than the LSTM network. That is because the cell structure of the GRU model is simpler than that of the LSTM model. On the issue of RUL prediction, GRU not only meets the accuracy requirements, but also owns a fast prediction speed. Therefore, the GRU network is the best solution to the RUL prediction of the hydrogen fuel cells among the abovementioned three networks.