电动汽车牵引逆变器可靠性与效率的多目标参数分析

L. Gill, L. Rashkin, L. Yates, J. Neely, R. Kaplar
{"title":"电动汽车牵引逆变器可靠性与效率的多目标参数分析","authors":"L. Gill, L. Rashkin, L. Yates, J. Neely, R. Kaplar","doi":"10.1109/APEC43580.2023.10131644","DOIUrl":null,"url":null,"abstract":"Transportation electrification is rapidly gaining momentum to reduce greenhouse gas emissions and carbon foot-prints. To help accelerate a swift transition to decarbonization, alternative modes of transportation, such as electric vehicles (EV) must provide superior performance and competitive advantages in regards to reliability (longevity), efficiency (fuel economy), and volumetric energy or power density (compact integration) in contrast to fossil fuel-powered transports. However, achieving optimum designs is challenging due to the multiple physical domain interactions between thermal, electrical, and mechanical systems within an EV drivetrain. Hence, this paper focuses on the multi-parametric design analysis of the EV traction inverter system to perform trade-off studies between two competing objectives: reliability and efficiency. A seamless performance evaluation process was developed between PLECS, a simulation platform for power electronic systems and the optimization computation of genetic algorithm based on NSGA-II in Python to achieve a reliable repetition of varied operating modes of the inverter to seek optimized parameters and non-dominant solutions. A realistic, high-fidelity, and multi-domain EV model based on the known physical parameters of Nissan Leaf was developed in PLECS along with a dynamic driving profile. The paper further discusses parametric design analysis and comparison based on different power module materials and operating conditions, such as EV battery voltage and power module switching frequency. The simulation results show that an optimized SiC solution can provide a higher efficiency design whereas higher reliability can be expected with the optimized IGBT-based designs.","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":"{\"title\":\"Multi-Objective Parametric Analysis of EV Traction Inverter between Reliability and Efficiency\",\"authors\":\"L. Gill, L. Rashkin, L. Yates, J. Neely, R. Kaplar\",\"doi\":\"10.1109/APEC43580.2023.10131644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Transportation electrification is rapidly gaining momentum to reduce greenhouse gas emissions and carbon foot-prints. To help accelerate a swift transition to decarbonization, alternative modes of transportation, such as electric vehicles (EV) must provide superior performance and competitive advantages in regards to reliability (longevity), efficiency (fuel economy), and volumetric energy or power density (compact integration) in contrast to fossil fuel-powered transports. However, achieving optimum designs is challenging due to the multiple physical domain interactions between thermal, electrical, and mechanical systems within an EV drivetrain. Hence, this paper focuses on the multi-parametric design analysis of the EV traction inverter system to perform trade-off studies between two competing objectives: reliability and efficiency. A seamless performance evaluation process was developed between PLECS, a simulation platform for power electronic systems and the optimization computation of genetic algorithm based on NSGA-II in Python to achieve a reliable repetition of varied operating modes of the inverter to seek optimized parameters and non-dominant solutions. A realistic, high-fidelity, and multi-domain EV model based on the known physical parameters of Nissan Leaf was developed in PLECS along with a dynamic driving profile. The paper further discusses parametric design analysis and comparison based on different power module materials and operating conditions, such as EV battery voltage and power module switching frequency. The simulation results show that an optimized SiC solution can provide a higher efficiency design whereas higher reliability can be expected with the optimized IGBT-based designs.\",\"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/APEC43580.2023.10131644\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Applied Power Electronics Conference and Exposition (APEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APEC43580.2023.10131644","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

交通电气化正在迅速获得动力,以减少温室气体排放和碳足迹。为了帮助加速向脱碳的快速过渡,与化石燃料驱动的交通工具相比,电动汽车(EV)等替代交通工具必须在可靠性(寿命)、效率(燃油经济性)和体积能量或功率密度(紧凑集成)方面提供卓越的性能和竞争优势。然而,由于电动汽车传动系统中热、电和机械系统之间存在多种物理领域的相互作用,因此实现最佳设计具有挑战性。因此,本文重点对电动汽车牵引逆变系统进行多参数设计分析,在可靠性和效率两个相互竞争的目标之间进行权衡研究。开发电力电子系统仿真平台PLECS与基于Python的NSGA-II遗传算法优化计算无缝对接的性能评估流程,实现逆变器各种运行模式的可靠重复,寻求最优参数和非优势解。基于已知的日产Leaf物理参数,在PLECS中开发了一个真实、高保真、多域的电动汽车模型,并提供了动态驾驶剖面。进一步针对不同的电源模块材料和电动车电池电压、电源模块开关频率等工况进行参数化设计分析与比较。仿真结果表明,优化后的SiC解决方案可以提供更高的效率设计,而优化后的基于igbt的设计可以提供更高的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Objective Parametric Analysis of EV Traction Inverter between Reliability and Efficiency
Transportation electrification is rapidly gaining momentum to reduce greenhouse gas emissions and carbon foot-prints. To help accelerate a swift transition to decarbonization, alternative modes of transportation, such as electric vehicles (EV) must provide superior performance and competitive advantages in regards to reliability (longevity), efficiency (fuel economy), and volumetric energy or power density (compact integration) in contrast to fossil fuel-powered transports. However, achieving optimum designs is challenging due to the multiple physical domain interactions between thermal, electrical, and mechanical systems within an EV drivetrain. Hence, this paper focuses on the multi-parametric design analysis of the EV traction inverter system to perform trade-off studies between two competing objectives: reliability and efficiency. A seamless performance evaluation process was developed between PLECS, a simulation platform for power electronic systems and the optimization computation of genetic algorithm based on NSGA-II in Python to achieve a reliable repetition of varied operating modes of the inverter to seek optimized parameters and non-dominant solutions. A realistic, high-fidelity, and multi-domain EV model based on the known physical parameters of Nissan Leaf was developed in PLECS along with a dynamic driving profile. The paper further discusses parametric design analysis and comparison based on different power module materials and operating conditions, such as EV battery voltage and power module switching frequency. The simulation results show that an optimized SiC solution can provide a higher efficiency design whereas higher reliability can be expected with the optimized IGBT-based designs.
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
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信