Prediction of Commuter Vehicle Demand Torque Based on Historical Speed Information

Abstract

The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry. In this paper, a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed, which uses machine learning to predict and analyze vehicle demand torque. Firstly, the big data of vehicle driving is collected, and the driving data is cleaned and features extracted based on road information. Then, the vehicle longitudinal driving dynamics model is established. Next, the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle, and the driving torque of the vehicle is obtained. Finally, the travel is divided into several acceleration-cruise-deceleration road pairs for analysis, and the vehicle demand torque is predicted by BP neural network and Gaussian process regression.

Keywords

demand torque prediction / commuter vehicle / historical driving data / machine learning