# Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network
## 2 Fault Correlation Analysis of Vehicle-Pile-Grid Charging
### Multistage Fault Correlation Analysis  
Power battery faults mainly include battery pack capacity reduction, the charging voltage is too high, the battery pack cannot charge, a low discharge voltage, large self-discharge, local high temperature, poor voltage consistency of the single cell, battery arc breakdown, single cell damage, etc., [23]. The fault phenomena of the charging equipment mainly include a charging gun fault, an abnormal program discrimination of the charging machine, an abnormal BMS report, an internal communication fault of the charging machine, a voltage and current fault, a communication terminal fault with BMS, an electronic lock opening and closing fault, an overtemperature fault, a mechanical fault of the charging module, etc., [24]. Distribution network faults include off-line voltage, system instability, etc., [25].  
Figure 1: Vehicle–Pile–Grid Integrated Safety Mechanism.  
An information flow model was used to analyse the correlation between faults [26]. The basic elements of an information flow model are tests, fault isolation conclusions, testable inputs, untestable inputs, and trouble-free conclusions. The model uses a directed graph to represent the causal relationship between tests and fault conclusions, in which the nodes are the test and the fault isolation conclusions, and the edges are the directions of information flow. In the information flow model, testing refers to all sources of information used to determine the health of a system, such as observed abnormal physical phenomena, abnormal data, and so on. \(T_{j}\) is used to represent the test in the model (\(j=1\),..., \(N\), \(N\) is the total number of tests in the system), all tests are binary, if \(t_{j}\) output is normal, it is \"0\", otherwise, it is \"1\". The test is symmetric if the information obtained at \"0\" is equivalent to that obtained at \"1\". In the model, each test is \"wrapped independently,\" meaning that each test can be carried out independently, regardless of order.  
The fault isolation conclusion includes all the failures of components, the components, subsystems and systems, special non-hardware faults, special multiple faults, lack of fault indication, abnormal system input and so on. The conclusion of the fault isolation is equivalent to the conclusion of the function diagnosis of each part of the system and is related to a certain maintenance level. In the model, \(f_{i}\) is used to represent the fault isolation conclusion (\(i=1\),..., \(M\), \(M\) is the total number of fault isolation conclusions in the system). When the fault corresponding to \(f_{i}\) occurs, \(f_{i}\) is \"1\", otherwise it is \"0\".  
The correlation information flow model was established according to the possible fault types of the vehicle-pile-grid analysed above and the information flow construction mode. \(T_{i}\) is the fault serial number, which is divided into SOC fault, over charge, over discharge, over temperature, spontaneous combustion, communication, software, mechanical, electrical, distribution network fault, and distribution network warning, etc. Distribution network outage failure will not charge the EV distribution network, the warning being relatively isolated. The relationship is shown in Figure 2.