A Machine Learning Based Fuel Consumption Saving Method with Time and Environment Dependency Aware Management.

Since the key factors affecting the fuel consumption of the long-distance vehicle are varied hourly on the whole truck running routes, which are long distance and stretched to various environmental district, the amount of saving fuel consumption must be limited until the following two things would have become able to do. The first is a better fuel-efficient driving control in terms of the low/high gear, accel, and the idling by increasing the driver's skills based on the training and driving assist systems that makes it possible to find own problem. And the second is a better mechanical maintenance to keep a better fuel-efficiency. Thus, this paper proposes a machine learning based time and environment condition dependency aware fuel consumption tracking method, which enables to give the timely feedback to the drivers and truck maintenance facility. The feedback will be given by making clearer what are the key factors affecting fuel consumption every period based on their time and environment dependencies analysis results. Since the various data obtained by the embedded sensors (CANbus &GPS) are complicated and its number of variables is over 100 (much larger than the conventional cases of less than 50), we initially used the LightGBM but we faced the bias vs variance tradeoff issue. To address this issue, we newly proposed the stacking scheme, which optimizes the trade-off by combining the random forest, the SVM, and the LightGBM. Thanks to this scheme, the variance (RMSE) error was reduced from 6.04 to 5.03, while keeping the best accuracy (R2) of 97.6%. We proposed the feedback system to reduce the fuel consumption based on the analysis of the key influencing factors that caused by the drivers. To our best knowledge, it is the first time to demonstrate that the saving of overall fuel consumption by the proposed feedback scheme can be reduced by 8% if some key factors could be carefully reduced by 50%.