Measurement of Vehicle Parameters for Vehicle Dynamics Model Validation

Abstract:

Vehicle dynamics models play an important role in understanding and predicting vehicle behavior under different operating conditions; however, their practical usefulness strongly depends on how well simulated responses reflect real vehicle performance. For this reason, model validation using real-world measurement data is essential. This study focuses on the validation of a MATLAB–Simulink-based longitudinal vehicle dynamics model using CAN bus data recorded during on-road driving. The measurement data were collected using a CANedge2 data logger, which enables the recording of key vehicle parameters such as vehicle speed, engine speed, brake pressure, and accelerator pedal position. The recorded MF4 files were decoded and processed using the asammdf software framework in combination with appropriate DBC files to extract physically meaningful signals. The measured signals were subsequently compared with the simulation results to assess the model’s ability to reproduce real vehicle behavior under different driving conditions. The presented workflow demonstrates a practical and reproducible approach for CAN-based model validation using real vehicle data and provides a basis for further refinement and extension of longitudinal vehicle models.

• Periodical: Advances in Science and Technology (Volume 181)
• Pages: 243-252
• DOI: https://doi.org/10.4028/p-YS8UuA
• Online since: August 2026
• Authors: Imola Lenke Csajbók*, Dániel Nemes
• Keywords: Data-Based Model, Model Validation, Optimization, Vehicle

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