{"title":"Fast Computational Method for PWM Strategy Comparison of Machine and Inverter Electrical Losses: Application on WLTC Cycle","authors":"Salma Benharref;Vincent Lanfranchi;Daniel Depernet;Tahar Hamiti;Sara Bazhar","doi":"10.1109/TIE.2024.3482006","DOIUrl":null,"url":null,"abstract":"For electric vehicle motors, it is essential to control the sources of loss which could reduce the overall efficiency of the system. In this context, this article aims to present a detailed comparison of machine and inverter losses between the space vector pulse width modulation (SVPWM) and the discontinuous pulse width modulation 2 (DPWM2). To compare both PWM strategies on the WLTC (worldwide harmonized light vehicles test cycles) cycle in an efficient way, a full analytical model is used to predict inverter losses and a sequential use of an analytical and a finite element analysis is used to perform an in-depth analysis of machine losses. This semianalytical method which can be used for any pulse width modulation (PWM) strategy allows to speed up the calculations and the comparison of different PWM schemes. Experiments were then conducted for different operating points and supply voltage levels to validate the results. The theoretical and experimental results show that although the DPWM2 allows to reduce inverter losses, it still generates more machine losses compared to the SVPWM especially on the WLTC cycle, which makes the DPWM2 less efficient. Nevertheless, depending on the total supply voltage, the DPWM2 could be more or less interesting on the WLTC cycle with comparison with the SVPWM.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 6","pages":"5549-5557"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10745991/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
For electric vehicle motors, it is essential to control the sources of loss which could reduce the overall efficiency of the system. In this context, this article aims to present a detailed comparison of machine and inverter losses between the space vector pulse width modulation (SVPWM) and the discontinuous pulse width modulation 2 (DPWM2). To compare both PWM strategies on the WLTC (worldwide harmonized light vehicles test cycles) cycle in an efficient way, a full analytical model is used to predict inverter losses and a sequential use of an analytical and a finite element analysis is used to perform an in-depth analysis of machine losses. This semianalytical method which can be used for any pulse width modulation (PWM) strategy allows to speed up the calculations and the comparison of different PWM schemes. Experiments were then conducted for different operating points and supply voltage levels to validate the results. The theoretical and experimental results show that although the DPWM2 allows to reduce inverter losses, it still generates more machine losses compared to the SVPWM especially on the WLTC cycle, which makes the DPWM2 less efficient. Nevertheless, depending on the total supply voltage, the DPWM2 could be more or less interesting on the WLTC cycle with comparison with the SVPWM.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.