{"title":"新一代电池储能系统SOC与电压预测的混合神经网络架构","authors":"G. Capizzi, F. Bonanno, C. Napoli","doi":"10.1109/ICCEP.2011.6036301","DOIUrl":null,"url":null,"abstract":"This paper presents some experiences and results obtained about the problem of the SOC and voltage prediction and simulation of new generation batteries. A complex pipelined recurrent neural network (PRNN) was designed for modeling of new generation batteries storage in order to predict the SOC and the terminal voltage. The simulation results are compared with experimental data obtained on commercial batteries.","PeriodicalId":403158,"journal":{"name":"2011 International Conference on Clean Electrical Power (ICCEP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":"{\"title\":\"Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage\",\"authors\":\"G. Capizzi, F. Bonanno, C. Napoli\",\"doi\":\"10.1109/ICCEP.2011.6036301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents some experiences and results obtained about the problem of the SOC and voltage prediction and simulation of new generation batteries. A complex pipelined recurrent neural network (PRNN) was designed for modeling of new generation batteries storage in order to predict the SOC and the terminal voltage. The simulation results are compared with experimental data obtained on commercial batteries.\",\"PeriodicalId\":403158,\"journal\":{\"name\":\"2011 International Conference on Clean Electrical Power (ICCEP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"34\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Clean Electrical Power (ICCEP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEP.2011.6036301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Clean Electrical Power (ICCEP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEP.2011.6036301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hybrid neural networks architectures for SOC and voltage prediction of new generation batteries storage
This paper presents some experiences and results obtained about the problem of the SOC and voltage prediction and simulation of new generation batteries. A complex pipelined recurrent neural network (PRNN) was designed for modeling of new generation batteries storage in order to predict the SOC and the terminal voltage. The simulation results are compared with experimental data obtained on commercial batteries.