# Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network
## 4 Simulation and Case Studies
### Simulation Analysis  
Five hundred groups of EV charging data were randomly selected to form the training set, and 200 groups of sampling data were selected as the test, and nine indicators at the indicator layer were taken as the neural network input characteristic factors. One hundred groups of initialization weight populations were randomly selected and the fitness of each weight was calculated. The initialization mutation probability parameter was set as 0.25, the initialization crossover probability parameter was set as 0.4, and the maximum iteration number was set as 500. The optimal initial weights are obtained by iterative genetic operators. The node weights of the hidden layer and output layer of 500 groups of training data are used. The learning efficiency of the optimal weight group \(\eta\) = 0.1, momentum factor \(A\) = 0.3, after 100 iterations, the error value of the neural network meets the precision requirement. The changes of training accuracy and mean square deviation are shown in the Figure 8 below.  
Figure 8 shows the accuracy changes of two kinds of neural network training. With the gradual increase of training times, its accuracy becomes higher and higher. It can be seen that after about 25 training sessions, the advantages of the GA_BP neural network become gradually obvious, and its accuracy gradually exceeds that of the BP neural network before improvement. Figure 9 shows the changing rule of the mean square error (MSE) of the BP neural network and the GA_BP neural network as the number of iterations increases. It can be seen from Figure 9 that, during the training process, the mean square error of the training group and the test group is approximately equal, and the mean square error of the GA_BP algorithm basically stays between 0.009 and 0.012, which is relatively small,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**T** & **Level** & **Score** \\ \hline
100/\(t_{1}\) & Absolutely safety & \textgreater{}90 \\
90/\(t_{2}\) & Safety & 80–90 \\
80/\(t_{3}\) & General safety & 70–80 \\
70/\(t_{4}\) & Dangerous & 60–70 \\
60/\(t_{5}\) & Very dangerous & \textless{}60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Safety grade rating form.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{5}{c}{**Hierarchy**} \\ \cline{2-5}  & **Absolutely Safety** & **General Safety** & **Dangerous** & \multicolumn{2}{c}{**Very**} \\  & **Safety** & & & & **Dangerous** \\ \hline Cumulative number of battery failures (times) & 1 & 3 & 5 & 10 & 15 \\ Battery SOC (\%) & 99 & 95 & 90 & 85 & 80 \\ Voltage difference of the battery pack (mV) & 10 & 50 & 100 & 200 & 500 \\ BMS response rate (\%) & 99.8 & 98.5 & 97 & 95 & 90 \\ Cumulative number of charging equipment & 0 & 2 & 4 & 5 & 8 \\ \multicolumn{5}{c}{Glarge output overvoltage rate (\%)} & 1.5 & 2.5 & 5 & 10 & 20 \\ Voltage level fluctuation rate of distribution & 0.3 & 1 & 2 & 3 & 5 \\ \multicolumn{5}{c}{Funculative number of distribution station} & 0 & 1 & 2 & 4 & 5 \\ \multicolumn{5}{c}{failures (times)} & 1 & 3 & 5 & 10 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The safety rating of the integrated safety evaluation index for charging electric vehicles.  
indicating that the accuracy of the network basically meets the calculation requirements. In comparison, the mean square error of the BP algorithm is relatively large, ranging from 0.060 to 0.070. In addition, it can be concluded that the training based on the GA_BP neural network has higher accuracy.