Construction of driving cycle for heavy-duty semi-trailer tractors based on sparsified data
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.
万方数据
维普
