基于国六商用车实际运行数据的行驶工况研究OA北大核心CSTPCD

To improve the accuracy of fuel consumption prediction based on driving cycles,an innovative approach to construct driving cycles for different market segments of heavy-duty commercial vehicles was proposed.To validate the imperative and rationality of the proposed approach,the daily industrial product market was used as an example to conduct a big data analysis of the driving conditions of National Ⅵ commercial vehicles.Based on the onboard Tianxingjian Intelligent Network System,driving data of 3 000 National Ⅵ series semi-trailer trucks in this market were collected.Through a series of steps,including kinematic segmentation,data dimensionality reduction,and segment chaining,three representative driving cycles were constructed.Based on this,a simulation model was built in AVL Cruise to predict the fuel consumption of the target market users and was compared with the actual fuel consumption and the predicted results based on legislative standard driving cycles of China world transient vehicle cycle(C-WTVC)and China heavy-duty commercial vehicle test cycle for tractor-trailer(CHTC-TT).The results show the constructed driving cycles for the industrial market segment are closer to the real-world driving characteristics of this market,with an average relative error reduction of 32.97 percentage points and 18.67 percentage points compared with C-WTVC and CHTC-TT for the characteristic parameters,respectively.It can also more accurately predict users'fuel consumption,with improvement in prediction accuracy of 7% and 4%,respectively.Therefore,the constructed driving cycles for target market segments of heavy-duty vehicles can effectively improve the prediction accuracy of driving characteristics and fuel consumption.