A State Monitoring and Multi-Level Safety Pre-Warning Method for Electric Vehicle Charging Process

被引：0
|
作者：
Gao D. [1 ]
Zheng X. [1 ]
Wang Y. [1 ]
Yang Q. [2 ]
机构：
[1] School of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao
[2] School of Information Science and Technology, Qingdao University of Science & Technology, Qingdao
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2022年 / 37卷 / 09期
关键词：
Bi-directional long-short term memory; Charging process; Convolutional neural networks; Electric vehicle; Multi-level safety pre-warning;
D O I：
10.19595/j.cnki.1000-6753.tces.211846
中图分类号：
学科分类号：
摘要：
The frequent occurrence of electric vehicle burning accidents during the charging process has become a key issue restricting the development of electric vehicles. Aiming at the problem of charging safety, this paper proposes a new method of electric vehicle charging status monitoring and multi-level safe pre-warning. The method is based on convolutional neural networks (CNN) and bi-directional long-short memory (BiLSTM), uses the charging history data of electric vehicles to construct a CNN-BiLSTM multi-level safety pre-warning model; designed the charging status monitoring and multi-level safe pre-warning implementation process of the model; compared with other models, verified the prediction accuracy of the model; through the sliding window method, the pre-warning threshold of the model is determined. Experiments have shown that this method can monitor the charging process of electric vehicles in real time, find faults in time and send out pre-warning signals to ensure the safety of electric vehicle charging. © 2022, Electrical Technology Press Co. Ltd. All right reserved.
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页码：2252 / 2262
页数：10
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  • [1] Qing Xiaodong, Su Yugang, An overview of electric-filed coupling wireless power transfer technology, Transactions of China Electrotechnical Society, 36, 17, pp. 3649-3663, (2021)
  • [2] Yabe K, Shinoda Y, Seki T, Et al., Market penetration speed and effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicles in Japan, Energy Policy, 45, pp. 529-540, (2012)
  • [3] Li Ming, Cui Song, Huang Haizhen, Et al., Effect of pipes in heat pump system on electric vehicle energy saving, International Journal of Green Energy, 17, 11, pp. 666-675, (2020)
  • [4] Tong Minghao, Cheng Ming, Xu Zhiyuan, Et al., Key issues and solutions of integrated on-board chargers for electric vehicles, Transactions of China Electrotechnical Society, 36, 24, pp. 5125-5142, (2021)
  • [5] Chen Zeyu, Xiong Rui, Sun Fengchun, Research status and analysis for battery safety accidents in electric vehicles, Journal of Mechanical Engineering, 55, 24, pp. 93-104, (2019)
  • [6] Gao Dexin, Wang Yi, Zheng Xiaoyu, Et al., A fault warning method for electric vehicle charging process based on adaptive deep belief network, World Electric Vehicle Journal, 12, 4, (2021)
  • [7] Fan Wenjie, Xu Guanghao, Yu Boning, Et al., On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy, Proceedings of the CSEE, 41, 9, pp. 3283-3293, (2021)
  • [8] Sun Jinlei, Zhu Chunbo, Li Lei, Et al., Online temperature estimation method for electric vehicle power battery, Transactions of China Electrotechnical Society, 32, 7, pp. 197-203, (2017)
  • [9] Xiao Qian, Jiao Zhipeng, Mu Yunfei, Et al., Prediction of battery remaining service life of electric vehicle under driving conditions based on lightGBM, Transactions of China Electrotechnical Society, 36, 24, pp. 5176-5185, (2021)
  • [10] Li Chaoran, Xiao Fei, Fan Yaxiang, Et al., An approach to lithium-ion battery SOH estimation based on convolutional neural network, Transactions of China Electrotechnical Society, 35, 19, pp. 4106-4119, (2020)
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