# Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network
## 3 The Integrated Charging Safety Evaluation Model Based on Fuzzy Neural Networks
### Fuzzy Comprehensive Evaluation  
The evaluation indicator of event \(T\) on top of the fault tree corresponds to the neurons of the output layer, which is a collection of synaptic weights in the network. The framework and diagram are as follows:  
For the complex electric vehicle integrated charging process, there are many evaluation predictors to consider, and the factors are at different levels. The application of single-level fuzzy evaluation analysis will not produce accurate evaluation results. Therefore, evaluation factors need to be classified by attribute, divided into power batteries, charging facilities and distribution networks, each category of evaluation, and then the results of various types of assessment of a multi-level comprehensive assessment [28]. 1. Determine the set of evaluation factors During charging, the safety problems are divided into \(n\) evaluation factors according to the predicted objects such as power batteries, charging equipment, and distribution network equipment: \[U\;=\;\{U_{i}\},\;i=1,2,\cdots,n\] (5) In the above, \(U_{i}\) represents the _ith_ factor in the system being evaluated. The collection of evaluation factors \(U_{i}\) is divided into \(m\) subsets by different failure types: \[U_{i}\;=\;\{u_{ij}\},\;j=1,2,\cdots,m\] (6)
2. Determine the review set In view of the safety influencing factors, the safety prediction evaluation indicator set is given, and the evaluation set is set for convenience \[V\;=\;\{v_{1},v_{2},\cdots,v_{s}\}\] (7) where: \(v_{k}\) indicates the result of the evaluation rating of things, \(s\) represents the number of evaluation grades, and is 5 in this paper. The integrated safety evaluation system of electric vehicles is rated. \(v_{1}\) means absolute security, \(v_{2}\) means security, \(v_{3}\) means general security, \(v_{4}\) means dangerous, \(v_{5}\) indicates very dangerous. Figure 4: Schematic diagram of fault tree embedded BP neural network. (3) Single-factor fuzzy evaluation  
Starting from a single factor in the factor set \(U\), it is assumed that the \(i\)th factor \(u_{i}\) has \(m\) fault types, and the \(j\)th fault type is represented by \(u_{ij}\). The membership of the \(k\)th comment level \(v_{k}\) is \(r_{jk}\), and \(k\) ranges from \(1\) to \(s\). Then the result of the \(i\)th evaluation factor is:  
\[R_{i}=\left[ \begin{array}{cccc}r_{11}&r_{12}&\cdots&r_{1s}\\ r_{21}&r_{22}&\cdots&r_{2s}\\ \vdots&\vdots&&\vdots\\ r_{m1}&r_{m2}&\cdots&r_{ms}\end{array} \right] \tag{8}\]  
(4) The establishment of the weight set  
In general, each fault type has a different degree of influence on the evaluation result, so each factor \(u_{i}\) is given a corresponding weight \(w_{ij}\) (\(j\) = 1,2,3,..., \(m\)), then the weight number of each factor constitutes the weight set \(w_{i}\):  
\[w_{i}=(w_{i1},w_{i2},\cdots,w_{im}) \tag{9}\]  
In the formula, \(w_{i}\) represents a fuzzy subset on \(u_{i}\) and meets the criteria:  
\[\sum_{j=1}^{m}w_{ij}=1\;and\;0<w_{ij}<1 \tag{10}\]  
(5) Single-factor fuzzy comprehensive evaluation  
Supposing you obtain the result of the \(i\)th factor fuzzy comprehensive evaluation after a fuzzy transformation:  
\[B_{i}=w_{i}\circ R\;=(w_{i1},w_{i2},\cdots,w_{im})\circ\left[ \begin{array}{ cccc}r_{11}&r_{12}&\cdots&r_{1s}\\ r_{21}&r_{22}&\cdots&r_{2s}\\ \vdots&\vdots&&\vdots\\ r_{m1}&r_{m2}&\cdots&r_{ms}\end{array} \right]=(b_{11},b_{12},\cdots,b_{is}) \tag{11}\]  
\(B_{i}\) refers to the fuzzy comprehensive evaluation result of category \(i\) factors, where \(\circ\) is a fuzzy operator, and the safety evaluation of EV charging involves multiple indicators from multiple aspects including battery, charging equipment, and distribution network. To avoid ignoring some key information and to get comprehensive evaluation results, the weighted summation generalized fuzzy operator \(M(\cdot,\oplus)\) is adopted in this paper when comprehensively considering the influence of all factors and the degree of membership of the evaluation object to each element in the evaluation set.