Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network
Source abstract
Abstract
Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Electric and Hybrid Vehicle Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automation, College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
State Grid Jiangsu Electric Power Co., Ltd., Research Institute, Nanjing 211103, China
NARI Technology Co., Ltd., Nanjing 211106, China
References (31)
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