Fault Diagnosis Model of New Energy Vehicle Charging Equipment Based on Fuzzy Fault Tree Theory

Wenqing Li

doi:10.54097/62h4x203

Authors

Wenqing Li

DOI:

https://doi.org/10.54097/62h4x203

Keywords:

Fuzzy fault tree; Internet of things; Charging equipment; New energy vehicles.

Abstract

This paper probes into the new energy automobile charging equipment fault diagnosis problem. Fuzzy fault tree theory is introduced as a solution, fuzzy mathematics is used to improve the accuracy of fault diagnosis, the fault diagnosis model of charging equipment is established, and the fault diagnosis system based on Internet of things technology is designed, including equipment layer, communication layer, server and client. The system aims to realize the real-time monitoring and processing of the fault of new energy vehicle charging facilities, improve the user experience and improve the safety index.

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References

Liu, J. (2023) Common Faults and Application of maintenance technology for pure electric vehicles. Special Purpose Vehicles, (1):69-71.

Gao, D., Liang, K., Hou, J., et al. (2019) Design of mobile monitoring and fault diagnosis system for electric vehicle charging pile. Journal of Qingdao University of Science and Technology (Natural Science Edition), 40(3):107-113.

Song, M., Kang, Y. (2022) Application and analysis of electric vehicle charging pile detection technology. Electrical Appliances and Energy Efficiency Management Technology, (3):77-80.

Song, H., Zhang, H., Wang, X. (2005) T-S Fuzzy Fault tree analysis method. Control and Decision, 20(8):854-859.

Shao, Y., Xue, H. (2007) Application of Fault tree analysis method in System fault diagnosis. Chinese Manufacturing Information Technology, 36(1):72-74,77.

Wei, X., Bian, S. (2004) Fault tree analysis and its application. Electronic products reliability and environmental testing, (3):43-45.

Zhao, Y., Gong, W. (2001) Research on Fuzzy fault tree analysis and its application. Chinese Journal of Safety Science, 11(6):31-35.

Zhu, D., Yu, S. (2002) Research on fault diagnosis method based on minimum cut set of fault tree. Data Acquisition and Processing, 17(3):341-344.

Yao, H., Li, M., Yang, X. (2021) Application of Fuzzy Fault tree Theory in Fault diagnosis of aviation charging equipment. Measurement and Testing Technology, 48(6):82-84.

Wu, W. (2023) Application of data mining in electric vehicle charging pile charging fault detection. Automobile and New Power, 6(2):81-84.

Gao, D., Zheng, X., Wang, Y., et al. (2022) Electric vehicle charging state monitoring and multi-level security forecasting police law . Transactions of China Electrotechnical Society, 37(9):2252-2262.

Xie, J, Yu, Q. (2022) Research on electric vehicle Battery Fault diagnosis based on fuzzy neural network. China New Technology and New Products, (6):20-23.

Ruan, Z., Jiao, H., Zhou, L., et al. (2023) Prospects and suggestions for deepening the application of Internet of Things in electric vehicle industry. Hubei Electric Power, 47(1):133-138.

Wu, J. (2019) Research on New energy vehicle operation monitoring Cloud platform based on big data and artificial intelligence. Journal of Guangdong Normal University of Technology, 40(6):19-25.

Miao, Y. (2020) The application of Internet of Things to electric vehicle charging facilities. Brand Research, (36):284.

Chen, Z. (2020) Application of artificial intelligence in automobile industry. Shanghai Quality, (3):28-31.

Yu, Y., Xue, X., Wang, H. (2022) Design of safety early warning system for new energy vehicles based on Internet of Things. Science and Technology and Innovation, (9):10-12,16.