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Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

For the last two decades, an extensive transition in automotive X-in-the-loop activities from isolated electronic control units to real-time related, geographically distributed validation tasks has occurred. Benefits are strengthening frontloading, enabling concurrent engineering and reducing prototypes and testing efforts. As a downside, comprehensive system understanding and adequate simulation models must be provided. New technological trends like software-over-the-air-updates denote a continuous validation process even after the start of production. The present review focuses on the virtual validation of vehicle longitudinal dynamics. This exemplary field of application receives more and more attention as electrification of the vehicle powertrain accelerates, and this property directly influences the vehicle DNA. A systematic review process based on the PRISMA workflow has been conducted, focusing on drivabilityrelated powertrain applications. The investigation reveals the following trends: First, increasing complexity of virtualisation methods and models for validation activities influenced by vehicle-to-everything and geographically distributed development. Second, missing standards for virtual validation and proof of representativeness for combined real-virtual testing. In addition, many studies only contemplate the advantages of hardware-in-the-loop-driven development, disregarding crucial limitations and risks for such approaches. In conclusion, there is no longer the question of whether to validate virtually but how to comprehensible realise virtual validation.

With the continuous digitalization of the vehicle development process, virtual design methods play an increasingly important role. Representative load profiles, which can be determined for example with the aid of multi-body system simulation, are required for the reliable design and validation of complex elastomeric bearings such as engine mounts for conventional and electrified drive trains. Due to the complex material properties and complex designs of today's elastomer and hydro mounts, the virtual representation of these components is a challenging task. In already published researches (46th Conference of the "DVM Arbeitsgruppe Betriebsfestigkeit") it could be shown that the current standard characterization of elastomeric bearings using static and dynamic stiffness as well as loss angle is only partly appropriate to build up and parameterize exact models for the virtual load data determination. On the basis of multi-axial load tests of selected engine mounts, which are done at the highly dynamic component test rig of the Chair of Automotive Engineering of the TU Dresden, physical effects with relevance for the durability could be determined. These are multi-axial static, dynamic and transient changes in stiffness and damping properties. With the knowledge of these effects, a complex model was developed which is designed for the representation of high dynamic loads, hydraulic damping and superposition effects of multi-axial excitations. The model is to be used primarily to calculate operating load signals using multi-body system simulation. Within the context of the research described here, a complex multistep parameter identification procedure based on particle swarm optimization is presented. With this method it is possible to use complex stochastic signals to identify the model parameters. The first step is to determine the static stiffness of the bearing model in the considered main direction. The uniaxial dynamic parameters are identified afterwards. In the following, the dependence of the uniaxial dynamic parameters on the deformation amplitude is evaluated. Finally, the parameters of the model elements are identified which are dependent on the deformation of the secondary directions (multi-axial model components). The described procedure allows a modularization of the complex bearing model, whereby the model complexity can be adapted according to the application. Multi-axial test rig measurements showed a significant improvement in the image quality of measured load signals compared to standard models for the newly developed model and the parameter identification process. The simulation error is used as evaluation criterion and the signal damage is considered by means of a standard fatigue strength evaluation. Different types of elastomeric and hydromounts are investigated for validation. Furthermore, the simulation quality of the modelling process outside the fitting range is evaluated. With the help of these investigations it can be deduced that the methods work robustly and will be suitable for practical use after a few adjustments. The investigations contribute to an improved and validated digital representation of elastomeric bearings with a focus on load data determination.