摘要
文章利用在柴油车上加装的车载远程监测设备,与车载诊断系统(on-board diagnostics,OBD)设备结合,采集车辆实际行驶工况下OBD的相关数据,通过对200辆重型柴油车连续10 d车辆数据分析处理,对影响车辆实际道路工况下NO_(x)排放的因素进行分析,结果表明,车辆实际道路工况下NO_(x)排放受车辆速度和加速度的双重影响。将采集到的行驶数据划分为4种工况,计算各工况下车辆的排放强度,运用统计学原理,计算出各行驶工况排放强度的标准差和均值,并得出各行驶工况下的排放强度限值,排放限值与车辆行驶速度成正比。最后筛选各行驶工况中重复车辆占比,结果表明:0~20 km/h速度区间下的筛选成功率最低,仅为48.33%;30~40 km/h和50~70 km/h速度区间下筛选成功率较为接近,在60%左右;成功率最高的工况为70~80 km/h,占到了68.43%;说明70~80 km/h速度区间下筛选出的超限车辆重复率最高,相应的高排放车辆筛选成功率最高。
Using the on-board remote monitoring equipment installed on diesel vehicles as well as the on-board diagnostics(OBD)equipment,this paper collects relevant OBD data under actual driving conditions of the vehicles,analyzes and processes the data of 200 heavy-duty diesel vehicles for ten consecutive days,and investigates the factors that affect the NO_(x) emission of the vehicles under the actual road conditions.The results show that the NO_(x) emission of the vehicles under the actual road conditions is dually affected by the vehicle speed and acceleration.The collected driving data is divided into four working conditions,and the vehicle emission intensity under each working condition is calculated.Using statistical principles,the standard deviation and average value of the emission intensity of each driving working condition are calculated to get the emission intensity limit under each driving condition,and the emission limit is proportional to the vehicle speed.Finally,the proportion of repeated vehicles in each driving condition is screened,and the results show that the screening success rate is the lowest in the speed range of 0-20 km/h,only 48.33%;at speeds of 30-40 km/h and 50-70 km/h,the screening success rate is relatively close,about 60%;the working condition with the highest success rate is 70-80 km/h,accounting for 68.43%;it indicates that the repetition rate of over-limit vehicles selected in the 70-80 km/h speed interval is the highest,and the corresponding screening success rate of high-emission vehicles is the highest.
作者
程晓章
王浩
邢晓通
钱赛
刘长波
CHENG Xiaozhang;WANG Hao;XING Xiaotong;QIAN Sai;LIU Changbo(School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《合肥工业大学学报（自然科学版）》
CAS
北大核心
2022年第7期894-900,共7页
Journal of Hefei University of Technology：Natural Science
基金
合肥工业大学产学研校企合作资助项目(2020BFFFJ00176)。
