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Without the masking effect of a combustion engine, noise from the road is much more prominent in electric vehicles (EV) and has become the dominant source of noise for drivers and passengers. Road noise however is a complex problem. Unlike engine noise, which comes from a single, well-defined source, road noise finds its origins in the road-to-tire contact. This means that there are typically 4 sources (assuming a 4-wheel vehicle) which are influenced by the roughness and profile of the road as well as the compliance of the tires. From an engineering point of view it’s easy to appreciate the added complexity compared to engine noise. In addition to the engineering complexity, there is also a supplier-OEM relationship that comes into play. Most OEMs do not manufacture their own tires and may even have multiple tire suppliers for the same vehicle. This brings on another set of complications.

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.

New challenges arise for engineers with the emergence of Hybrid Electrical Vehicles (HEV) and Battery Electrical Vehicles (BEV). As an electric motor has a lower overall noise emission compared to an Internal Combustion Engine (ICE), the overall noise level inside an EM driven car’s cabin is more colored by road noise and windnoise. That being said, the EM can still be very audible because of its prominent high-pitched tonal content, linked to the machine design (number of slots, poles). Hence, in view of driver and passenger comfort, reduction of cabin noise also involves optimizing the noise mission of electric motors. The noise produced by an electrical machine in operation can be assessed early on in the design process using simulation methods. This paper presents currently available approaches for assessing during the ideation phase the noise induced by a brushless Permanent Magnet Synchronous Machine (PMSM) which are widely used in electrical vehicles.

Radars based ADAS solutions are growing at a substantial rate in the automotive market since they play a fundamental role in increasing passengers and pedestrian safety. To support radar designers in reducing time and design cost, to meet the stringent vehicle safety norms, to reduce risks about final installed radar performance etc., more effective and efficient industrial approach have to be devised both in terms of simulation (working at 77 GHz poses several numerical challenges) and testing. This paper presents IDS/Siemens PLM Software technical approach and CAE tools, based on a synergic use of high-fidelity simulation models and measurement set-up’s to support the whole design, optimization and verification cycle, starting from the stand-alone radar antenna performance up to on-car installed radar operational performance verification.

Improving fuel efficiency often affects NVH performance. Modifying a vehicle’s design in the latter stages of development to improve NVH performance is often costly. Therefore, to optimize the cost performance, a Multi-Attribute Balancing (MAB) approach should be employed in the early design phases. This paper proposes a solution based on a unified 1D system simulation model across different vehicle performance areas. In the scope of this paper the following attributes are studied: Fuel economy, Booming, Idle, Engine start and Drivability. The challenges to be solved by 1D simulation are the vehicle performance predictions, taking into account the computation time and accuracy. Early phase studies require a large number of scenarios to evaluate multiple possible parameter combinations employing a multi-attribute approach with a systematic tool to ease setup and evaluation according to the determined performance metrics.

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).

Brake squeal has been a persistent quality issue for automobile OEMs and brake system suppliers. The ability to model and measure brake squeal dynamics is of utmost importance in brake squeal reduction efforts. However, due to the complex nature of brake squeal and the wide frequency range in which it occurs, it is difficult to accurately correlate and update analytical models to experimental results. This paper introduces a systematic and rigorous correlation and updating process that yields FE models, which can accurately reproduce high-frequency brake squeal dynamics.