傅里叶和拉普拉斯变换用于神经网络电池建模的预处理

Proceedings of The 7th International Power Electronics and Motion Control Conference Pub Date : 2012-06-02 DOI:10.1109/IPEMC.2012.6258892

J. Hu, Changhong Liu, Xuguang Li

{"title":"傅里叶和拉普拉斯变换用于神经网络电池建模的预处理","authors":"J. Hu, Changhong Liu, Xuguang Li","doi":"10.1109/IPEMC.2012.6258892","DOIUrl":null,"url":null,"abstract":"To solve the large storage capacity in neural network battery modeling, an input pretreatment, based on Fourier or Laplace transform, is proposed. As simulation shows, the improved battery model gets a better precision and consumes a smaller storage capacity. This method can also be used in SOC estimation if farther experiments are conducted.","PeriodicalId":236136,"journal":{"name":"Proceedings of The 7th International Power Electronics and Motion Control Conference","volume":"127 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fourier and laplace transform used in pretreatment for neural network battery modeling\",\"authors\":\"J. Hu, Changhong Liu, Xuguang Li\",\"doi\":\"10.1109/IPEMC.2012.6258892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To solve the large storage capacity in neural network battery modeling, an input pretreatment, based on Fourier or Laplace transform, is proposed. As simulation shows, the improved battery model gets a better precision and consumes a smaller storage capacity. This method can also be used in SOC estimation if farther experiments are conducted.\",\"PeriodicalId\":236136,\"journal\":{\"name\":\"Proceedings of The 7th International Power Electronics and Motion Control Conference\",\"volume\":\"127 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of The 7th International Power Electronics and Motion Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPEMC.2012.6258892\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of The 7th International Power Electronics and Motion Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPEMC.2012.6258892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

针对神经网络电池建模中存储容量大的问题，提出了一种基于傅里叶变换或拉普拉斯变换的输入预处理方法。仿真结果表明，改进后的电池模型具有更高的精度和更小的存储容量。在进一步的实验中，该方法也可用于SOC的估算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Fourier and laplace transform used in pretreatment for neural network battery modeling

To solve the large storage capacity in neural network battery modeling, an input pretreatment, based on Fourier or Laplace transform, is proposed. As simulation shows, the improved battery model gets a better precision and consumes a smaller storage capacity. This method can also be used in SOC estimation if farther experiments are conducted.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of The 7th International Power Electronics and Motion Control Conference

自引率

0.00%

发文量