电动汽车用LiFePO4电池放电模拟器-第2部分:电池模拟器的开发

2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE) Pub Date : 2015-05-07 DOI:10.1109/ATEE.2015.7133926

B. Enache, M. Alexandru, L. Constantinescu

{"title":"电动汽车用LiFePO4电池放电模拟器-第2部分:电池模拟器的开发","authors":"B. Enache, M. Alexandru, L. Constantinescu","doi":"10.1109/ATEE.2015.7133926","DOIUrl":null,"url":null,"abstract":"This paper, the second of the series on developing a LiFePO4 (LFP) battery discharge simulator for EV applications, presents an approach for developing the battery simulator. After the optimal battery model was established, in the first paper, the main drawbacks of battery simulator are addressed and a new strategy is proposed. This strategy uses neural networks (NN) for online estimation of the battery parameters during discharge. The proposed simulator is validated by comparisons with a typical simulator and data from a LFP battery submitted to discharge profile in concordance with a driving cycle.","PeriodicalId":103513,"journal":{"name":"2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A LiFePO4 battery discharge simulator for EV applications — Part 2: Developing the battery simulator\",\"authors\":\"B. Enache, M. Alexandru, L. Constantinescu\",\"doi\":\"10.1109/ATEE.2015.7133926\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper, the second of the series on developing a LiFePO4 (LFP) battery discharge simulator for EV applications, presents an approach for developing the battery simulator. After the optimal battery model was established, in the first paper, the main drawbacks of battery simulator are addressed and a new strategy is proposed. This strategy uses neural networks (NN) for online estimation of the battery parameters during discharge. The proposed simulator is validated by comparisons with a typical simulator and data from a LFP battery submitted to discharge profile in concordance with a driving cycle.\",\"PeriodicalId\":103513,\"journal\":{\"name\":\"2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)\",\"volume\":\"81 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ATEE.2015.7133926\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATEE.2015.7133926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

本文是开发EV应用的LiFePO4 (LFP)电池放电模拟器系列的第二篇，介绍了开发电池模拟器的方法。在建立了最优电池模型后，本文首先分析了电池模拟器存在的主要缺陷，并提出了一种新的策略。该策略利用神经网络(NN)对电池放电过程中的参数进行在线估计。通过与典型的模拟器和LFP电池的数据进行比较，验证了所提出的模拟器的有效性。

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

A LiFePO4 battery discharge simulator for EV applications — Part 2: Developing the battery simulator

This paper, the second of the series on developing a LiFePO4 (LFP) battery discharge simulator for EV applications, presents an approach for developing the battery simulator. After the optimal battery model was established, in the first paper, the main drawbacks of battery simulator are addressed and a new strategy is proposed. This strategy uses neural networks (NN) for online estimation of the battery parameters during discharge. The proposed simulator is validated by comparisons with a typical simulator and data from a LFP battery submitted to discharge profile in concordance with a driving cycle.

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

2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

自引率

0.00%

发文量