首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Development of driving cycles for electric vehicles in the context of the city of Florence
Institution:1. Transportation College of Jilin University, Changchun 130022, China;2. China FAW Group Corporation R&D Center, Changchun 130025, China;1. School of Transportation, Jilin University, Changchun 130025, China;2. Shenzhen’s Key Laboratory of Traffic Information and Traffic Engineering, Shenzhen 518021, China;3. School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China;1. Dept of Environ. Eng., Faculty of Technical Engineering, Aleppo Univ., Syria;2. Institute of Engineering and Energy Technologies, School of Engineering and Computing, University of the West of Scotland, Paisley PA1 2BE, UK
Abstract:Strong efforts are spent in automotive engineering for the creation of so called Driving Cycles (DCs). Vehicle DC development has been a topic under research over the last thirty years, since it is a key activity both from an authority and from an industrial research point of view. Considering the innovative characteristics of Electric Vehicles (EVs) and their diffusion on certain contexts (e.g. city centers), the demand for tailored cycles arises. A proposal for driving data analysis and synthesis has been developed through the review and the selection of known literature experiences, having as a goal the application on a EVs focused case study. The measurement campaign has been conducted in the city of Florence, which includes limited traffic areas accessible to EVs. A fleet of EVs has been monitored through a non-invasive data logging system. After data acquisition, time-speed data series have been processed for filtering and grouping. The main product of the activity is a set of DCs obtained by pseudo-randomized selection of original data. The similarity of synthetic DCs to acquired data has been verified through the validation of cycle parameters. Finally, the new DCs and a selection of existing ones are compared on the basis of relevant kinematic parameters and expected energy consumption. The method followed for the creation of DCs has been implemented in a software package. It can be used to generate cycles and, under certain boundary conditions, to get a filtered access to the measured data and provide integration within simulation environment.
Keywords:Driving cycle  Grouping  Speed  Electric vehicles  Regeneration  Random walk
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号