摘要
针对纯电动汽车的驱动电机特有的转速转矩特性,使电动汽车变速箱不需要太多挡位的问题,本文在某款电动汽车基础上,对传动比进行初步匹配,运用实验设计(design of experiments,DOE)对传动比进行分析,并对传动比进行多目标优化。优化结果表明,优化后的电机转矩较优化前减小,传动比的改变可以使电机的转矩趋于更加合理的区间,避免了大转矩低需求的能耗浪费现象,优化后,最高车速提升了5.6 km/h,在0~100 km/h时,加速时间增加了3.7 s,最大爬坡度减少了7.7%,但是百公里油耗减少了2.4 kWh,说明最高车速比优化前略有提高,百公里能量消耗比优化前略有减少,该结果满足预期设置的动力性指标,且提高了经济性能。该研究具有广阔的应用前景。
Due to the unique speed and torque characteristics of pure electric vehicle drive motors,electric vehicle gearboxes do not require too many gears.This article is based on a certain type of electric vehicle that matches the transmission ratio,and performs multi-objective optimization design of the transmission ratio through experimental design(design of experiments,DOE).The optimization results show that the torque of the motor after optimization is smaller than that before optimization.The change of the transmission ratio can make the torque of the motor tend to a more reasonable interval,and avoid energy waste with high torque and low demand.The optimized maximum speed increased by 5.6 km/h,the acceleration time increased by 3.7 s,and the maximum climbing capacity decreased by 7.7%,but the fuel consumption per 100 kilometers decreased by 2.4 kWh.This indicates that the maximum speed ratio has increased,compared with that before optimization,and it reduces energy consumption by 100 kilometers.The above shows that the results are consistent with the expected dynamic indicators,and economic performance is improved.This research has broad application prospects.
作者
王聪聪
张洪信
赵清海
贾腾飞
侯典平
WANG Congcong;ZHANG Hongxin;ZHAO Qinghai;JIA Tengfei;HOU Dianping(School of Electromechanic Engineering,Qingdao University,Qingdao 266071,China;Power Integration and Energy Storage Systems Engineering Technology Center,Qingdao University,Qingdao 266071,China)
出处
《青岛大学学报（工程技术版）》
CAS
2020年第1期58-64,共7页
Journal of Qingdao University(Engineering & Technology Edition)
基金
山东省自主创新及成果转化专项(40215020073)
山东省科技发展计划项目(2014GGX103044)
青岛市战略性新兴产业培育计划项目(14-8-1-2-gx)
关键词
纯电动汽车
传动比匹配
DOE分析
多目标优化
electric vehicles
ratio matching
doe analysis
multi-objective optimization
