Prediction Study of the Heavy Vehicle Driving State Based on Digital Twin Model

In order to study the driving state of heavy vehicles, two approaches are employed hereby to establish digital twin models for analyzing the applicable scopes of the models and conducting a predictive study. To begin with, the operating parameters and the state of the vehicles are measured using instruments and apparatuses. Then, relying on a GP and deep convolutional neural network (DCNN), two digital twin models of vehicles driving state are established, respectively, which set the transmission system and power system parameters as well as weather conditions as input parameters; and vehicle running speed and torque value as output ones. Both digital twin models consider the physical rule of the vehicle to avoid overfitting in the training. The analytical results indicate that the GP-based digital twin model appears more accurate in predicting the driving parameters of the vehicles, whereas the model based on the DCNN has better convergence precision within a short span of time. The vehicle-specific digital twin models set up in this paper lay a foundation for subsequent optimization of vehicle driving state and realization of digital twin-physical entity interaction.