Zhaopeng Li, Jie Pang, Chunxiang Xu, Chengcai Zhang
{"title":"电动汽车充电系统压力控制基于模糊神经网络PID控制","authors":"Zhaopeng Li, Jie Pang, Chunxiang Xu, Chengcai Zhang","doi":"10.1109/TOCS53301.2021.9688659","DOIUrl":null,"url":null,"abstract":"This paper proposes a charging system for electric vehicles and a fuzzy neural network PID control system for the non-linear variation of system contact pressure. Firstly, the constitution of the charging system is introduced, and the working principle of the system is described. Secondly the unsteady change in the contact pressure of the system caused by vehicle vibration is analyzed, and the fuzzy neural network PID control system is designed for the above problems. Then the system model is built by simulink software for simulation analysis. Finally, the simulation results show that the performance of the fuzzy neural network PID control is better than the traditional PID control, and it can effectively control the contact pressure of the system in a more stable working state.","PeriodicalId":360004,"journal":{"name":"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"570 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Electric vehicle charging system pressure control based on fuzzy neural network PID control\",\"authors\":\"Zhaopeng Li, Jie Pang, Chunxiang Xu, Chengcai Zhang\",\"doi\":\"10.1109/TOCS53301.2021.9688659\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a charging system for electric vehicles and a fuzzy neural network PID control system for the non-linear variation of system contact pressure. Firstly, the constitution of the charging system is introduced, and the working principle of the system is described. Secondly the unsteady change in the contact pressure of the system caused by vehicle vibration is analyzed, and the fuzzy neural network PID control system is designed for the above problems. Then the system model is built by simulink software for simulation analysis. Finally, the simulation results show that the performance of the fuzzy neural network PID control is better than the traditional PID control, and it can effectively control the contact pressure of the system in a more stable working state.\",\"PeriodicalId\":360004,\"journal\":{\"name\":\"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)\",\"volume\":\"570 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TOCS53301.2021.9688659\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TOCS53301.2021.9688659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electric vehicle charging system pressure control based on fuzzy neural network PID control
This paper proposes a charging system for electric vehicles and a fuzzy neural network PID control system for the non-linear variation of system contact pressure. Firstly, the constitution of the charging system is introduced, and the working principle of the system is described. Secondly the unsteady change in the contact pressure of the system caused by vehicle vibration is analyzed, and the fuzzy neural network PID control system is designed for the above problems. Then the system model is built by simulink software for simulation analysis. Finally, the simulation results show that the performance of the fuzzy neural network PID control is better than the traditional PID control, and it can effectively control the contact pressure of the system in a more stable working state.