基于稀疏化数据的重载半挂牵引车工况构建  

Construction of driving cycle for heavy-duty semi-trailer tractors based on sparsified data

作　　者：王磊 刘洪利 李彬[2] 侯圣栋 WANG Lei;LIU Hongli;LI Bin;HOU Shengdong(Institute of Automotive,Shaanxi Heavy Duty Automobile Co.,Ltd.,Xi’an 710200,China;School of Automobile,Chang’an University,Xi’an 710064,China)

机构地区：[1]陕西重型汽车有限公司汽车工程研究院,西安710200 [2]长安大学汽车学院,西安710064

出　　处：《中国科技论文》2024年第10期1115-1124,共10页China Sciencepaper

基　　金：国家自然科学基金资助项目(52272354);中央高校基本科研业务费专项资金资助项目(300102223208)。

摘　　要：针对北方环境下重载半挂牵引车跨区域典型行驶工况的缺位现象,以及工况构建对优质数据的高度依赖问题,应用道格拉斯-普克(Douglas-Peucker,DP)算法将采集数据处理为稀疏节点数据与驾驶行为特征数据,基于稀疏节点数据划分行驶片段,经主成分分析和k-means聚类构建初始工况,初始工况与驾驶行为特征数据匹配和叠加形成典型行驶工况。结果表明,该工况构建方法的平均相对误差相比传统方法降低了约19.63%,其仿真油耗与原始数据仿真油耗的相对误差也在5%以内。同时,该方法可以实现低频数据向高频数据的映射,有助于解决工况构建对采集数据颗粒度的高度敏感性问题。In view of the lack of typical cross-region driving cycles for heavy-duty semi-trailer tractors in northern environments and the high dependence on high quality data in their construction,the Douglas-Peucker algorithm was applied to process the collected data into sparse node data and driving behavior data.Based on the sparse node data driving segments was divided aiming at construct⁃ing the initial cycle by principal component analysis and k-means clustering.Then the initial cycle was matched with the driving behavior data to form the typical driving cycle.The results show that the mean relative error of this method is about 19.63%lower than that of the traditional method,and its simulated fuel consumption(SFC)exhibits a relative error less than 5%from the SFC of the original data.Meanwhile,this method can realize the mapping of low-frequency data to high-frequency data,which may contrib⁃ute to the reduction of the sensitivity of cycle construction to the granularity of the collected data.

关 键 词：重载半挂牵引车 行驶工况 轨迹数据 道格拉斯-普克算法 

分 类 号：U461.8[机械工程—车辆工程]