Time and Environment Dependency Aware Fuel Consumption Tracking Method for Improving Drivers and Trucks Management
Time and Environment Dependency Aware Fuel Consumption Tracking Method for Improving Drivers and Trucks Management
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用于改善驾驶员和卡车管理的时间和环境依赖性感知燃油消耗跟踪方法
DOI：
10.1109/itc-cscc52171.2021.9501436
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Jiazheng Xi and Hiroyuki Yamauchi
中科院分区：
文献类型：
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作者：
Peng Yu;Jiazheng Xi and Hiroyuki Yamauchi
除非在适应时间和环境变化的同时能够做到以下两件事，否则节省油耗的水平肯定是不幸的有限的，因为影响油耗的关键因素因具体情况而每小时不同：1)诸如齿轮/加速/空转之类的驾驶控制可以由每个驾驶员调整，以及2)每辆卡车可以得到最好的机械维护，以提高燃油效率。因此，本文提出了一种时间和环境条件依赖感知的油耗跟踪方法，该方法能够在考虑司机和卡车维修机构的时间和环境依赖的同时，更加清楚地了解每个时期影响油耗的关键因素是什么，从而能够及时地向司机和卡车维修机构提供反馈。针对嵌入式传感器(CAN Bus和GPS)获取的数据复杂、数据量大的特点，采用LightGBM模型及时提取关键因素，并与其他支持向量机和随机森林(RF)模型的预测精度和可获得的抽象信息进行比较。结果表明，三种模型的预测准确率最高，达到98.7%。Shap值用于解释关键影响因素，用于反馈给每个驾驶员进行调整。研究发现，如果一些关键因素能够谨慎地降低50%，则总体油耗可以降低16%。
Until the following two things would have become able to do while adapting to the changes of the time and environment, the level of saving fuel consumption must be unfortunately limited because the key factors affecting fuel consumption are varied hourly on a case by case: 1) driving controls such as gear/accel/idling can be adjusted by each driver and 2) each truck can be mechanically best maintained so as to improve the fuel efficiency. Thus, this paper proposes a time and environment condition dependency aware fuel consumption tracking method, which enables to give a timely feedback to the drivers and truck maintenance facility by making more clear what are the key factors affecting fuel consumption every period while considering their time and environment dependencies. Since the data obtained by embedded sensors (canbus&GPS) are complicated and its size is very huge, the LightGBM is used to timely abstract the key factors in this work, while comparing the prediction accuracy and available abstract information with the other models of SVM and Random Forest (RF). The results have shown that the best accuracy of 98.7% was achieved among the three models. SHAP value is used to explain the key influencing factors, which is used for feedback to each driver for his adjustment. It was found that overall fuel consumption can be reduced by 16% if some key factors could be carefully reduced by 50%.