Research on Intelligent Vehicle Tracking Control and Energy Consumption Optimization Based on Dilated Convolutional Model Predictive Control

Lanxin Li, Wenhui Pei () and Qi Zhang ()
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Lanxin Li: Shandong Key Laboratory of Technologies and Systems for Intelligent Construction Equipment, Shandong Jiaotong University, Jinan 250357, China
Wenhui Pei: Shandong Key Laboratory of Technologies and Systems for Intelligent Construction Equipment, Shandong Jiaotong University, Jinan 250357, China
Qi Zhang: School of Control Science and Engineering, Shandong University, Jinan 250061, China

Energies, 2025, vol. 18, issue 10, 1-22

Abstract: To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking control method based on a dilated convolutional neural network (DCNN). First, an effective dataset was constructed by incorporating historical state information, such as longitudinal tire forces and vehicle speed, to accurately capture vehicle dynamic characteristics and reflect energy variations during motion. Next, a dilated convolutional vehicle system model (DCVSM) was designed by combining vehicle dynamics with data-driven modeling techniques. This model was then integrated into a model predictive control (MPC) framework. By solving a nonlinear optimization problem, a dilated convolutional model predictive controller (DCMPC) was developed to enhance tracking accuracy and reduce energy consumption. Finally, a co-simulation environment based on CarSim and Simulink was used to evaluate the proposed method. Comparative analyses with a traditional MPC and a neural network-based MPC (NNMPC) demonstrated that the DCMPC consistently exhibited superior trajectory tracking performance under various test scenarios. Furthermore, by computing the tire-slip energy loss rate, the proposed method was shown to offer significant advantages in improving energy efficiency.

Keywords: intelligent driving; dilated convolutional neural network; model predictive control; trajectory tracking; energy consumption (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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