Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel
{"title":"单轴驱动电动汽车的节能扭矩分配","authors":"Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel","doi":"10.1109/IVS.2014.6856499","DOIUrl":null,"url":null,"abstract":"We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Energy-efficient torque distribution for axle-individually propelled electric vehicles\",\"authors\":\"Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel\",\"doi\":\"10.1109/IVS.2014.6856499\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.\",\"PeriodicalId\":254500,\"journal\":{\"name\":\"2014 IEEE Intelligent Vehicles Symposium Proceedings\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Intelligent Vehicles Symposium Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2014.6856499\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Intelligent Vehicles Symposium Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2014.6856499","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy-efficient torque distribution for axle-individually propelled electric vehicles
We propose a novel operation strategy for electric vehicles with axle-individual electric machines to improve their energy efficiency in typical driving situations. The developed algorithm is allocating a total torque requested by a velocity controlling system or the driver to the electric machines such that the energy loss is reduced compared to an equal distribution. By taking near-future forecasts into account, the predictive nature of the algorithm leads to a minimized number of clutching processes compared to previous work and thereby contributes to increased comfort and minimized component wear. Overall, an average reduction of up to 25% in the electric machine losses can be achieved for the ARTEMIS driving cycles. At the same time, a reduction of the clutching operations by 70% is possible due to the forecast, compared to algorithms only considering the momentary state.