Non-Invasive Identification of Vehicle Suspension Parameters: A Methodology Based on Synthetic Data Analysis

Alfonso de Hoyos Fernández de Córdova (), José Luis Olazagoitia and Carlos Gijón-Rivera
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Alfonso de Hoyos Fernández de Córdova: Industrial Engineering and Automotive Department, Nebrija University, Sta. Cruz de Marcenado 27, 28015 Madrid, Spain
José Luis Olazagoitia: Faculty of Design, Innovation and Technology, University of Design, Innovation and Technology (UDIT), Av. Alfonso XIII, 97, 28016 Madrid, Spain
Carlos Gijón-Rivera: School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico

Mathematics, 2024, vol. 12, issue 3, 1-31

Abstract: In this study, we introduce an innovative approach for the identification of vehicle suspension parameters, employing a methodology that utilizes synthetic and experimental data for non-invasive analysis. Central to our approach is the application of a basic local optimization algorithm, chosen to establish a baseline for parameter identification in increasingly complex vehicle models, ranging from quarter-vehicle to half-vehicle (bicycle) models. This methodology enables the accurate simulation of the vehicle dynamics and the identification of suspension parameters under various conditions, including road perturbations such as speed bumps and curbs, as well as in the presence of noise. A significant aspect of our work is the ability to process real-world data, making it applicable in practical scenarios where data are obtained from onboard sensor equipment. The methodology was developed in MatLab, ensuring portability across platforms that support this software. Furthermore, the study explores the application of this methodology as a tool for denoising, enhancing its utility in real-world data analysis and predictive maintenance. The findings of this research provide valuable insights for vehicle suspension design, offering a cost-effective and efficient solution for dynamic parameter identification without the need for physical disassembly.

Keywords: suspension parameter identification; vehicle dynamics simulation; basic local optimization; predictive vehicle analysis; non-invasive suspension testing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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