L. Lusuardi, A. Cavallini, V. Madonna, P. Giangrande, M. Galea
{"title":"车辆牵引电动机的非常规加速热老化试验","authors":"L. Lusuardi, A. Cavallini, V. Madonna, P. Giangrande, M. Galea","doi":"10.1109/EIC47619.2020.9158744","DOIUrl":null,"url":null,"abstract":"Car manufacturers are increasingly working to make the all-electric car a reliable and affordable mobility option for a larger section of the population. However, several issues need to be addressed before achieving this goal. One of the problems is represented by the qualification of the insulation systems employed in electric motors. Unfortunately, the tests to be performed are time consuming and the design of such measurements very often is done by trial and error, further lengthening the validation phase. In addition, since cars are exposed to variable torque and speed operations (especially when driven in city traffic), the stator winding temperature is expected to vary in a wide range. Nevertheless, indications regarding the impact that highly variable temperature conditions play on the insulation reliability are not available, at the moment. In this work, the results of accelerated thermal aging tests using temperature profiles (i.e. variable temperature ageing) are presented and discussed. The experiments are accomplished on simplified insulation systems for electric motor (i.e. specimens) and cycling temperature profiles, ranging between 200°C – 260°C and featuring different thermal gradients, are applied as ageing stress. Finally, the possibility of relying on statistical techniques for improving the quality of prediction, while shortening the testing time, is explored.","PeriodicalId":286019,"journal":{"name":"2020 IEEE Electrical Insulation Conference (EIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unconventional accelerated thermal ageing test for traction electric motors in vehicles\",\"authors\":\"L. Lusuardi, A. Cavallini, V. Madonna, P. Giangrande, M. Galea\",\"doi\":\"10.1109/EIC47619.2020.9158744\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Car manufacturers are increasingly working to make the all-electric car a reliable and affordable mobility option for a larger section of the population. However, several issues need to be addressed before achieving this goal. One of the problems is represented by the qualification of the insulation systems employed in electric motors. Unfortunately, the tests to be performed are time consuming and the design of such measurements very often is done by trial and error, further lengthening the validation phase. In addition, since cars are exposed to variable torque and speed operations (especially when driven in city traffic), the stator winding temperature is expected to vary in a wide range. Nevertheless, indications regarding the impact that highly variable temperature conditions play on the insulation reliability are not available, at the moment. In this work, the results of accelerated thermal aging tests using temperature profiles (i.e. variable temperature ageing) are presented and discussed. The experiments are accomplished on simplified insulation systems for electric motor (i.e. specimens) and cycling temperature profiles, ranging between 200°C – 260°C and featuring different thermal gradients, are applied as ageing stress. Finally, the possibility of relying on statistical techniques for improving the quality of prediction, while shortening the testing time, is explored.\",\"PeriodicalId\":286019,\"journal\":{\"name\":\"2020 IEEE Electrical Insulation Conference (EIC)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Electrical Insulation Conference (EIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIC47619.2020.9158744\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Electrical Insulation Conference (EIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIC47619.2020.9158744","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unconventional accelerated thermal ageing test for traction electric motors in vehicles
Car manufacturers are increasingly working to make the all-electric car a reliable and affordable mobility option for a larger section of the population. However, several issues need to be addressed before achieving this goal. One of the problems is represented by the qualification of the insulation systems employed in electric motors. Unfortunately, the tests to be performed are time consuming and the design of such measurements very often is done by trial and error, further lengthening the validation phase. In addition, since cars are exposed to variable torque and speed operations (especially when driven in city traffic), the stator winding temperature is expected to vary in a wide range. Nevertheless, indications regarding the impact that highly variable temperature conditions play on the insulation reliability are not available, at the moment. In this work, the results of accelerated thermal aging tests using temperature profiles (i.e. variable temperature ageing) are presented and discussed. The experiments are accomplished on simplified insulation systems for electric motor (i.e. specimens) and cycling temperature profiles, ranging between 200°C – 260°C and featuring different thermal gradients, are applied as ageing stress. Finally, the possibility of relying on statistical techniques for improving the quality of prediction, while shortening the testing time, is explored.