Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a practice-oriented overview of simulation methods in NVH development methodology for electric drive units. This includes target cascading and multi-objective optimisation, and by attacking NVH at the source using KPIs early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning (Neural Network) algorithms are utilized to find the best-in-class design, using multi-objective optimisation as well as refining simulation accuracy by adding tolerance effects while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, using simulation for next-generation electric powertrain development. Several applications and real-life examples

Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef

A Self-Learning Framework for NVH CAE Analysis

2026-01-0718

6/20/2026

The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort, yet optimizing for Noise, Vibration, and Harshness (NVH)—specifically road noise—presents a persistent challenge in the modern automotive development cycle. While advanced Finite Element Method (FEM) analysis is essential, the increasing complexity and volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or compromises in final comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that integrates conventional CAE simulation workflows with advanced machine learning in an iterative, cyclic process. This creates an exceptionally user-friendly and self-correcting system that autonomously defines, performs, and learns from computational experiments. By leveraging machine learning algorithms to build robust predictive models

Visser, Rene

Active Learning Supported Metadata Extraction from NVH Simulations

2026-01-0720

6/20/2026

Simulations can only be searched, reused and leveraged as training data for machine learning methods if suitable metadata are related. Manually obtaining these metadata is time-consuming and requires expert knowledge. Consequently, there often is a lack of metadata and this prohibits the reutilization of simulation data. Therefore, automated frameworks for metadata extraction are essential to obtain metadata information quickly, effortlessly and cost-efficiently. At present, there are no toolboxes for Finite-Element-Simulation data. Nevertheless, machine learning methods are a promising solution for this task. Training classical supervised machine learning methods for metadata generation often faces the lack of labeled data since manual labelling can be very costly. Therefore, rule-based extraction algorithms are used as an alternative for fundamental metadata extraction. For more enhanced tasks they are often not feasible. Active Learning is a suitable technique to overcome this

Luegmair, Marinus, Gröttrup, Sören

Joint Estimation of Inputs and States for Multi-Axis Vibration Environment Testing

2026-01-0683

6/20/2026

Monitoring inputs and states of a structural dynamic system is often challenging, as direct measurements are costly or even infeasible. A virtual sensing methodology is presented for jointly estimating the input and state of a structure when subjected to multi-directional base excitations. The approach uses a tuned Kalman Filter combined with a model-order reduction of the system model to ensure a low computational cost whilst allowing accurate estimation from a limited number of acceleration measurements. This enables real-time virtual health monitoring strategies and reduction in instrumentation during data acquisition without additional information such as location and direction of application about the inputs. The proposed methodology is validated numerically and experimentally using a notched aluminum beam excited on a multi-directional shaker table, driven simultaneously in two in-plane directions. The study demonstrates accurate full-field estimation of multiple responses along

Salazar Colunga, Rodrigo, Pandiya, Nimish, Dindorf, Christian, Naets, Frank

AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan

2026-01-0716

6/20/2026

In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design stage, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which

Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha

NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping

2026-01-0719

6/20/2026

Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, funded by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched open Sentinel-2 satellite imagery, OpenStreetMap road data and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. A case study in an urban environment (Graz, Austria) demonstrates the model’s applicability. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current

Girstmair, Josef

An Experimental Setup for Vibroacoustic and Fluid-Born Noise Characterization of Electric Refrigerant Compressors Using R1234yf or R744

2026-01-0724

6/20/2026

The increasing electrification of vehicles means that heating, ventilation and air conditioning systems have a broader range of tasks and a different priority assessment. In electric cars, air conditioning systems are not only responsible for cooling the passenger compartment, but also for controlling the battery temperature, particularly during rapid charging, which represents a high-load operating point. Furthermore, achieving high thermodynamic efficiency is desirable, as this directly impacts the range of electric cars. The elimination of the combustion engine as a major source of noise prioritizes the noise, vibration and harshness behavior of the refrigerant compressor for product selection. To investigate the vibration and acoustic behavior, as well as the fluid dynamic forces resulting from the cyclic compression principle of an electric refrigerant compressor, a test rig was developed that allows compressors to be operated and measured in isolation in an anechoic chamber under

Beer, Gabriel, Saur, Lukas, Schwarz, Manuel, Zemsch, Stefan, Becker, Stefan

Influence of Common-Mode Voltage Reduction Techniques on High-Frequency Sideband Vibrations in IPMSM Drives for EVs

2026-01-0695

6/20/2026

Space vector pulse width modulation (SVPWM) induces common-mode voltage (CMV) in three-phase voltage-source inverters, producing steep voltage edges that can lead to high leakage currents. In electric drive applications, these currents accelerate motor bearing degradation and may cause winding insulation failure. Active-zero-state PWM (AZSPWM) and near-state PWM (NSPWM) have been proposed as alternative modulation strategies to mitigate CMV and reduce drive degradation. This paper investigates the noise, vibration, and harshness performance of AZSPWM and NSPWM in comparison with conventional SVPWM. The proposed CMV reduction schemes are evaluated in terms of both CMV mitigation and their impact on high-frequency sideband vibration harmonics. Experimental results demonstrate that the CMV reduction strategies are highly effective in lowering CMV levels relative to SVPWM; however, this benefit is accompanied by an increase in vibration levels, which may adversely affect the mechanical

Khamis, Mahmoud Aly, Tatar, Andrei Alexandru, Repecho, Victor, Doria-Cerezo, Arnau

Regularized Inverse Methods for Tire Noise Source Identification and Pass-by Noise Estimation in Electric Vehicles

2026-01-0709

6/20/2026

Tire exterior noise has become increasingly critical in vehicle acoustics due to two key developments: updated pass-by noise regulations, which amplify the relative contribution of tire noise, and the rise of Battery Electric Vehicles (BEVs), which lack traditional powertrain noise. Design trends in BEVs—such as increased vehicle mass from battery packs and the widespread use of large-diameter, wide, low-profile tires—further intensify tire noise due to stiffer constructions and altered contact dynamics. A common method for predicting tire noise is the source-transfer-receiver model, where the tire is represented by a set of monopoles with volume velocity Q derived from near-field measurements. Acoustic propagation is modeled via p/Q transfer functions. Despite its simplifications, this approach is practical for vehicle development, enabling clear separation between source and transfer mechanisms and facilitating targeted noise control strategies. In previous work, we proposed a

Morin, Benjamin, Di Marco, Federico, Horak, Jan, Lafont, Thibault, Kim, Minkyu, Kang, Min Kyoo, Yoo, Ji Woo

Dynamic Characterisation and Modelling of Electrical Cables under Representative Load Conditions

2026-01-0676

6/20/2026

Electric high voltage (HV) cables are commonly used in automotive applications and very prominently in electrified vehicles. These cables are potential flanking transmission paths for structure-borne sound in a broad frequency range and must therefore be included in the NVH design process. Electrical high voltage cables exhibit non-linear mechanical characteristics, when exposed to significant bending the internal geometry of the cable will change and a curvature dependent bending stiffness will result. The electrical cables envisaged in the current publication feature a helically wound stranded aluminium wire core. This conductive core is covered by, in sequence, a silicone rubber insulation, a braided aluminium wire shield with aluminium foil to minimize electromagnetic interference and a silicone rubber outer sheath. An extensive measurement campaign was carried out to dynamically characterize cable specimen of different lengths and cross sections in terms of multi-degree of freedom

Nijman, Eugene, Buchegger, Blasius, Böhler, Elmar, Zeller, Bernhard, Rejlek, Jan, Faksa, Lukáš, Lukavsky, David

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