抄録
Modeling fuel consumption based on driving states is essential in developing eco-driving and route selection strategies for advanced driver assistance systems. However, accurately predicting vehicle fuel consumption is challenging due to dynamic factors like road slope, vehicle conditions, and rapid changes in driving behavior. Developed based on ideal road and vehicle conditions, traditional empirical models often fail to adapt to these conditions, leading to inaccurate estimations. This study explores incorporating throttle position, a critical indicator of driving activity regarding motion control, acceleration, and speed, to enhance predictive accuracy, overcoming the unknown slope effects. Since the throttle position indicating the applied torque to the engine has a complicated relationship associated with engine and vehicle speeds, gear, and road slope, machine learning methods are employed to develop the model instead of typical empirical models, like VT-Micro, for standard driving. The results show that the Random Tree performed best on the training dataset (RMSE= 0.2396, R2 = 0.9859). They are further evaluated on a cross-dataset, which is not used for training, where Neural Network outperformed all models (RMSE = 0.4462, R2 = 0.9545). In contrast, VT-Micro consistently exhibited poor accuracy, particularly in transient driving conditions (RMSE = 2.0282, R2 = 0.0591). These findings underscore the advantages of using activity information with machine learning for modeling complex, nonlinear vehicle dynamics, providing more accurate and adaptive fuel consumption predictions for sustainable transportation planning and energy management.
https://ror.org/046fm7598 
