Security Analysis and Protection for Charging Protocol of Smart Charging Pile

Xu, Jiangpei; Yu, Xiao; Tian, Li; Wang, Jie; Liu, Xiaojun

doi:10.1007/978-981-15-3753-0_95

Cited by 2 publications

(2 citation statements)

References 6 publications

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Section: Fault Correlation Analysis Of Vehicle-pile-grid Chargingmentioning

confidence: 99%

“…Currently, safety of charging is a major research direction. The existing studies on the safety of electric vehicles mainly start from the independent per spectives of power batteries [10] and the safety protection of charging equipment [20]. To study the safety of EV charging in a more comprehensive and in-depth way, this chapte integrates the three aspects of the EV power battery, charging equipment, and power grid The safety mechanism and correlation of electric vehicle charging are analyzed, and the factors affecting charging safety are also analyzed to provide theoretical basis for subse quent research on electric vehicle charging safety assessment.…”

Section: The Safety Mechanism Of Vehicle-pile-grid Chargingmentioning

confidence: 99%

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Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

et al. 2022

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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.

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Section: Fault Correlation Analysis Of Vehicle-pile-grid Chargingmentioning

confidence: 99%

Section: The Safety Mechanism Of Vehicle-pile-grid Chargingmentioning

confidence: 99%