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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.

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.

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.