Xie Bingyan, Li Tiezhu, Liu Tianhao, Chen Haibo, Li Hu, Li Ying
School of Transportation, Southeast University, Nanjing, China.
Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
Sci Total Environ. 2024 Nov 15;951:175443. doi: 10.1016/j.scitotenv.2024.175443. Epub 2024 Aug 10.
To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid "deceleration-uniform-acceleration" process, which significantly contributes to high NOx and CO emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.
为揭示重型柴油车（HDDV）在上坡和下坡行驶时出现的突出高排放问题，本研究提出一种从发动机原理角度描述由坡度引起的氮氧化物（NOx）和二氧化碳（CO）高排放驾驶行为的方法。通过基于车载诊断（OBD）数据和数字高程模型（DEM）数据计算HDDV的排放和坡度数据，首先通过基于规则的短行程分割方法获得包括上坡、平路和下坡的262个短行程，并通过肯德尔秩相关系数（Kendall's Tau）和K均值聚类验证短行程的道路坡度与排放之间的显著相关性。其次，通过比较三种速度类别（加速状态、匀速状态和减速状态）的分布变化，讨论不同坡度水平下HDDV运行状态的差异。最后，使用机器学习模型（随机森林、XGBoost和弹性网络）开发NOx和CO排放估计模型，识别高排放驾驶行为，特别是在上坡驾驶期间，其高排放比例最高。通过特征重要性和SHapley加性解释（SHAP）模型解释，大油门踏板开度、频繁急加速和高发动机负荷对NOx和CO排放均有正向影响。不同之处在于空燃比，发动机处于浓或微稀燃烧状态会增加CO排放，而稀燃烧状态会增加NOx排放。此外，由于实际爬坡情况的不确定性，驾驶员经常会经历快速的“减速-匀速-加速”过程，从发动机角度来看，这对高NOx和CO排放有显著贡献。研究结果为设计坡度场景下的驾驶策略提供了见解，并为描述驾驶行为提供了新的视角。
