Electrothermal Modeling Based Digital Twin Method for Degradation Parameters Identification of DC-DC Converter

Chuangchuang Lu, Weiyang Zhou, Ke-feng Jin
{"title":"Electrothermal Modeling Based Digital Twin Method for Degradation Parameters Identification of DC-DC Converter","authors":"Chuangchuang Lu, Weiyang Zhou, Ke-feng Jin","doi":"10.1109/APEC43580.2023.10131640","DOIUrl":null,"url":null,"abstract":"For most digital twin (DT) method, it is challenge to achieve high accuracy parameters identification due to the digital model is not a perfect replica of physical model. In this paper, a novel DT approach based on electrothermal model, which features more realistic than ideal model, is firstly proposed to obtain accurate parameters identification. By calculating the power loss of the proposed digital model, the temperature of self-heating devices in the digital model, such as MOSFETs, diodes and capacitors, can be obtained, so that the values of the temperature-dependent parameters in these devices can be updated, and hence more accurate results can be guaranteed. The proposed method is validated on a 500W buck converter and the experimental results show that the maximum estimated error of the on-state resistances of MOSFET is 0.6%, which is hundreds of times higher accuracy than conventional DT methods.","PeriodicalId":151216,"journal":{"name":"2023 IEEE Applied Power Electronics Conference and Exposition (APEC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Applied Power Electronics Conference and Exposition (APEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APEC43580.2023.10131640","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

For most digital twin (DT) method, it is challenge to achieve high accuracy parameters identification due to the digital model is not a perfect replica of physical model. In this paper, a novel DT approach based on electrothermal model, which features more realistic than ideal model, is firstly proposed to obtain accurate parameters identification. By calculating the power loss of the proposed digital model, the temperature of self-heating devices in the digital model, such as MOSFETs, diodes and capacitors, can be obtained, so that the values of the temperature-dependent parameters in these devices can be updated, and hence more accurate results can be guaranteed. The proposed method is validated on a 500W buck converter and the experimental results show that the maximum estimated error of the on-state resistances of MOSFET is 0.6%, which is hundreds of times higher accuracy than conventional DT methods.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于电热建模的DC-DC变换器退化参数识别数字孪生方法
对于大多数数字孪生方法来说,由于数字模型不是物理模型的完美复制品,难以实现高精度的参数识别。本文首次提出了一种新的基于电热模型的DT方法,该方法具有比理想模型更真实的特点,可获得准确的参数识别。通过计算所提出的数字模型的功率损耗,可以得到数字模型中自热器件(如mosfet、二极管和电容器)的温度,从而可以更新这些器件中温度相关参数的值,从而保证更准确的结果。在500W降压变换器上对该方法进行了验证,实验结果表明,MOSFET导通电阻的最大估计误差为0.6%,比传统的DT方法精度提高了数百倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Advanced Front-end Monitoring Scheme for Inductive Power Transfer Systems Based on Random Forest Regression An MPC based Power Management Method for Renewable Energy Hydrogen based DC Microgrids Overview of Machine Learning-Enabled Battery State Estimation Methods Ultra-Wideband Unidirectional Reset-Less Rogowski Coil Switch Current Sensor Topology for High-Frequency DC-DC Power Converters Common Source Inductance Compensation Technique for Dynamic Current Balancing in SiC MOSFETs Parallel Operations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1