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The automotive industry is evolving towards Electrified Vehicles (EV) in the recent years. Compared to the traditional ICE vehicles, tire noise induced by the tire-road interaction, is no longer masked by the internal combustion engine, and therefore becomes one of the most dominant sources of noise within the cabin and acoustic emission perceived by by-standers. Robust source characterization is one of the most important tasks for road noise prediction. The receiver-independent tire blocked forces are often used as ire-road source characteristics, which can be applied to any test-based or FE-based vehicle model to obtain the interior noise. They can be inversely identified from measurements on a tire test rig or on an in-situ vehicle. However, this inverse process needs to be repeated for different tires, roads and rolling speeds, which can become time-consuming and expensive.

With the electrification trend in the automotive industry, the main contributors to in-vehicle noise profile are represented by drivetrain, road and wind noise. To tackle the problem in an early stage, the industry is developing advanced techniques guaranteeing modularity and independent description of each contributor. Component-based Transfer Path Analysis (C-TPA) allows individual characterization of substructures that can be assembled into a virtual vehicle assembly, allowing the manufacturers to switch between different designs, to handle the increased number of vehicle variants and increasing complexity of products. A major challenge in this methodology is to describe the subsystem in its realistic operational boundary conditions and preload. Moreover, to measure such component, it should be free at the connection interfaces, which logically creates significant difficulties to create the required conditions during the test campaign.

ADAS system development for safety as well as comfort is a major activity for all AOEMs and dedicated system suppliers. The development of these systems relies heavily on the availability of accurate driving dynamics models to validate the control algorithms and verify vehicle performance in realistic driving scenarios. AOEMs usually have access to high-fidelity full vehicle models but the availability of such models poses a significant challenge to software and sub-system suppliers. In order for them to develop their ADAS solutions, an accurate test-based model identification process using prototype or benchmark vehicles would be highly desirable. In this paper, a stepwise approach for the identification of the vehicle parameters in order to create an accurate 15-DOF vehicle dynamics model is proposed. The approach relies on an optimized use of vehicle driving test data to minimize test bench or laboratory testing.

In the frame of automotive Noise Vibration and Harshness (NVH) evaluation, inner cabin noise is among the most important indicators. The main noise contributors can be identified in engine, suspensions, tires, powertrain, brake system, etc. With the advent of E-vehicles and the consequent absence of the Internal Combustion Engine (ICE), tire/road noise has gained more importance, particularly at mid-speed driving and in the spectrum up to 300 Hz. At the state of the art, the identification and characterization of Noise and Vibration sources rely on pointwise sensors (microphones, accelerometers, strain gauges). Optical methods such as Digital Image Correlation (DIC) and Laser Doppler Vibrometer (LDV) have recently received special attention in the NVH field because they can be used to obtain full-field measurements.

A key metric of a car body structure is the body stiffness, which shows significant correlation with different vehicle performance attributes as NVH, comfort and vehicle handling. Typical approaches to identify static stiffness characteristics are the use of a static stiffness test bench or the ‘static-from-dynamic’ approach in which free-free acquired transfer functions are used to build a modal model from which the static stiffness characteristics are extracted. Both of these approaches have limitations, the static stiffness bench with respect to clamping conditions and reproducing those in CAE, the static-from-dynamic with respect to the modal analysis (EMA) that needs to be performed. EMA is a subjective process, which can limit result robustness. In addition, performing EMA on a trimmed body is difficult due to the high modal density and the high level of damping.

The source-transfer-receiver model to approach automotive NVH problems has proven its worth over the last decades. The approach allows splitting up an NVH problem into a source, for example engine vibration or road induced wheel vibration, a transfer system, for example the car body or car suspension, and a receiver such as the driver ear or steering wheel feeling. The analysis of such a system is called Transfer Path Analysis (TPA). Whereas the determination of the transfer system for a TPA analysis through frequency transfer functions or a set of modes is fairly straightforward, the source side can pose quite some difficulties. For the sake of this paper, the sources are defined as the forces acting on the body structure of a car through the engine (for an engine noise problem) or suspension mounts (for a road noise problem).

Tip-in/Tip-out of the accelerator pedal generates transient torque oscillations in the driveline. These oscillations may be amplified by P/T, suspension and body modes and will eventually be sensible at the receiver side in the vehicle, for example at the seat or at the steering-wheel. The forces that are active during this transient excitation are influenced by non-linear effects in both the suspension and the power train mounts. In order to understand the contribution of each of these forces to the total interior target response (e.g. seat rail vibration) a detailed investigation is performed. Traditional force identification methods are not suitable for low-frequent, transient phenomena like tip-in/tip-out. Mount stiffness method can not be used because of non-linear effects in the P/T and suspension mounts. Application of matrix inversion method based on trimmed body vibration transfer functions is not possible due to numerical condition problems.

Road-tire induced vibrations are in many vehicles determining the interior noise levels in (semi-) constant speed driving. The understanding of the noise contributions of different connections of the suspension systems to the vehicle is essential in improvement of the isolation capabilities of the suspension- and body-structure. To identify these noise contributions, both the forces acting at the suspension-to-body connections points and the vibro-acoustic transfers from the connection points to the interior microphones are required. In this paper different approaches to identify the forces are compared for their applicability to road noise analysis. First step for the force identification is the full vehicle operational measurement in which target responses (interior noise) and indicator responses (accelerations or other) are measured.

As vehicle development timelines continue to shorten and more emphasis is put on simulating vehicle dynamic phenomena; the importance of having physically correct inputs increases. For modeling the road noise phenomena, there are some methods used in the industry such as application of experimental spindle forces or vertical displacements applied to the tire patch. Each of these has limitations with respect to absolute accuracy or dependency of the input on suspension characteristics. For accurate evaluation of new designs, an invariant input which can reproduce measured vehicle cabin response is beneficial. Specifically, it is desired that significant improvement can be made over the spindle force method. To this end, a tire model derived from experimental data has been developed, along with three degree of freedom tire patch input displacements.