Novel Neural-Network-Based Fuel Consumption Prediction Models Considering Vehicular Jerk

Abstract

Conventional fuel consumption prediction (FCP) models using neural networks usually adopt driving parameters, such as speed and acceleration, as the training input, leading to a low prediction accuracy and a poor correlation between fuel consumption and driving behavior. To address this issue, the present study introduced jerk (an acceleration derivative) as an important variable in the training input of four selected neural networks: long short-term memory (LSTM), recurrent neural network (RNN), nonlinear auto-regressive model with exogenous inputs (NARX), and generalized regression neural network (GRNN). Furthermore, the root-mean-square error (RMSE), relative error (RE), and coefficient of determination (R2) were used to evaluate the prediction performance of each model. The results from the comparison experiment show that the LSTM model outperforms the other three models. Specifically, the four selected neural network models exhibited an improved accuracy in fuel consumption prediction after the jerk was added as a new variable to the training input. LSTM exhibited the greatest improvement under the high-speed expressway scenario, in which the RMSE decreased by 14.3%, the RE decreased by 28.3%, and the R2 increased by 9.7%.