# Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network
## 4 Simulation and Case Studies
### Case Study  
Nanjing is one of the cities in China vigorously promoting electric vehicles. In recent years, the government has been strengthening the construction of the charging equipment infrastructure, and more users are now running electric vehicles. Therefore, the charging data of five groups of electric vehicles in a specific area in Nanjing in 2020 were selected for safety evaluation and analysis. The EVs in the five sets of data include EVs that are used shortly after purchase and EVs that have been driven for many years. The charging facilities include newly built charging stations and charging stations with long service life. In this way, the charging situation of the charging vehicles can be more comprehensively reflected. The comprehensive fuzzy evaluation method is used for data safety evaluation and analysis, and the obtained charging data is shown in Table 4.  
Figure 8: Comparison before and after neural network optimization. Figure 9: Change of mean square error before and after improvement of the BP neural network. The two indicators of battery SOC and BMS response rate of the charging equipment are the larger the better index. The rest are the smaller the better index. The membership degree matrix is established according to the relevant membership function [31]:  
\[R_{1}=\left[ \begin{array}{ccccc}0.453&0.276&0.246&0.025&0\\ 0.354&0.458&0.161&0.027&0\\ 0.245&0.387&0.343&0.025&0\end{array} \right] \tag{22}\]  
\[R_{2}=\left[ \begin{array}{ccccc}0.615&0.306&0.079&0&0\\ 0.378&0.504&0.109&0.009&0\\ 0.265&0.464&0.108&0.094&0.069\end{array} \right] \tag{23}\]  
\[R_{3}=\left[ \begin{array}{ccccc}0.223&0.487&0.164&0.126&0\\ 0.106&0.558&0.209&0.105&0.022\\ 0.195&0.253&0.369&0.183&0\end{array} \right] \tag{24}\]  
According to the GA_BP neural network, the fuzzy indexes of power battery safety, charging equipment safety and distribution network safety are obtained. Taking A group as an example, the single-layer fuzzy evaluation matrix is calculated:  
\[R=\left[ \begin{array}{ccccc}W_{1}R_{1}\\ W_{2}R_{2}\\ W_{3}R_{3}\end{array} \right]=\left[ \begin{array}{ccccc}B_{1}\\ B_{2}\\ B_{3}\end{array} \right]=\left[ \begin{array}{ccccc}0.4015&0.3376&0.2354&0.0255&0 \\ 0.5208&0.3690&0.0891&0.0130&0.0081\\ 0.1911&0.4592&0.2126&0.1319&0.0050\end{array} \right] \tag{25}\]  
Then according to Formula (13) one can find the electric vehicle integration evaluation degree evaluation sub-judgment matrix:  
\[B=W\times R=\left[ \begin{array}{ccccc}0.4073&0.3607&0.1940&0.0352&0.0027\\ 0.4037&0.3589&0.1998&0.0351&0.0024\\ 0.3891&0.3676&0.1978&0.0427&0.0028\end{array} \right] \tag{26}\]  
Finally, based on the Formula (14) to get the charging all-in-one safety score value:  
\[f=B\times T=\left[ \begin{array}{ccccc}91.34&91.26&90.97\end{array} \right] \tag{27}\]  
Similarly, based on the B-E group charging data calculated in accordance with the above steps, the resulting five sets of calculations are calculated, and the corresponding safety rating is expressed in Table 5 as follows. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **A** & **B** & **C** & **D** & **E** \\ \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 (\%) & 99.5 & 98.3 & 92.6 & 97.4 & 95.7 \\ Cumulative number of charging equipment failures (times) & 0 & 2 & 5 & 3 & 4 \\ Charge output overvoltage rate (\%) & 2.4 & 3.2 & 11 & 4.1 & 5.6 \\ Voltage level fluctuation rate of distribution network (\%) & 0.7 & 5.4 & 6.3 & 1.4 & 2.1 \\ Cumulative number of distribution station failures (times) & 1 & 4 & 3 & 0 & 2 \\ Power over-line rate of distribution station (\%) & 1.8 & 10.6 & 8.5 & 2.6 & 5.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Five groups of data of electric vehicle charging in a specific area of Nanjing in 2020.