Using onboard accelerometers to measure dynamic vehicle responses is a promising method for modern condition monitoring of railway tracks, as well as for optimising rail vehicle dynamics to improve comfort and safety [1]. Understanding vehicle-track interaction and the resulting dynamics is crucial for providing reliable, meaningful measurement data and accurate interpretation. Furthermore, creating measurements for research purposes can be challenging, as real-world measurements are costly and timeconsuming. Digital twins, on the other hand, provide the possibility of creating artificial measurement data quickly, remotely, and in a comparable and repeatable way under varying conditions. Currently, these track monitoring methods often use simple physical digital twin modelling to describe the transfer path from vehicle-track interaction to the sensor position. Additionally track irregularities serve as stimuli for these models. However, most measurement vehicles are older and/or have been drastically modified, so parameter values for specific vehicle components may be unavailable, withheld by the manufacturer, or have changed over time. Hence, previous research has focused on simple spring-damper systems [2]. In this presentation the approach is to use onboard axle-box acceleration sensors as well as real track data to identify the vehicle component parameters and provide an improved multi-body digital twin of the measurement vehicle. Various parameter identification approaches are tested, based on real-world measurements acquired by a research and inspection vehicle operating on main lines in the Netherlands. The resulting digital twin can be used not only for track monitoring, but also to support design decisions regarding ideal sensor placement, predict the consequences of vehicle modifications, and assist in comparing data from different vehicles. [1] Baasch, Benjamin und Oselin, Pierfrancesco und Groos, Jorn Christoffer. ¨ Model-based and datadriven digital twins for railway vehicle-track interaction monitoring.The 9th European Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS Congress 2024. [2] Sansinena, Adri ˜ an, Rodr ´ ´ıguez-Arana, Borja, Arrizabalaga, Saioa (2025). A systematic review of acceleration-based estimation of railway track quality. Vehicle System Dynamics, 1–28, 2025.