Preparation for Parameter Identification in a Vehicle Dynamics Model

Abstract

Vehicle dynamics models are important for understanding and predicting vehicle behavior under different operating conditions. However, their practical applicability depends largely on how well the simulated responses reflect the actual performance of the vehicle. Therefore, it is essential to validate the model with real measurement data. This study focuses on the validation of a MATLAB–Simulink longitudinal vehicle dynamics model and the parameter identification of rolling resistance using CAN bus data recorded during road driving. The measurement data were collected using a CANedge2 data logger. The measured signals were compared with the simulation results to assess the model’s ability to reproduce real vehicle behavior under different driving conditions. The resistance forces causing the vehicle to decelerate were also examined. The presented workflow demonstrates a practical and reproducible approach for CAN-based model validation using real vehicle data.