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Apart of other aspects, the interior sound of a passenger car brand has to meet customer expectations. For optimizing the sound of a passenger car, target sounds have first to be established via the operating range of the vehicle. For an effective sound engineering approach an objective description and evaluation of vehicle interior sound is beneficial. Such an objective description guarantees the effective and reproducible implementation of the required brand sound in the vehicle development process. In such a process it is necessary to reduce on the one hand annoying undesired noise aspects and to create on the other hand the relevant and necessary noise parameters to meet the target sounds head on.

The time reduction of the vehicle design and creation process is an important key to reduce development costs. Modern CAD systems offer a wide range of possibilities, not only in the standard field of mechanical design, but also in terms of creating advanced control mechanisms concerning part and assembly structures. Integrated design strategies include miscellaneous scripting and programming possibilities as well as implemented functions. An efficient application of these features can help to decrease the development time and to manage the growing functional complexity of automotive engineering processes. Continuously increasing product variants and functionalities call for design strategies and methods, which are capable to handle a quick data control and data transfer supporting an efficient geometry creation and evaluation.

The paper focuses on an optimization methodology, which uses Design of Experiments (DoE) methods to define component parameters of parallel hybrid powertrains such as number of gears, transmission spread, gear ratios, progression factor, electric motor power, electric motor nominal speed, battery voltage and cell capacity. Target is to find the optimal configuration based on specific customer targets (e.g. fuel consumption, performance targets). In the method developed here, the hybrid drive train configuration and the combustion engine are considered as fixed components. The introduced methodology is able to reduce development time and to increase output quality of the early system definition phase. The output parameters are used as a first hint for subsequently performed detailed component development. The methodology integrates existing software tools like AVL CRUISE [5] and AVL CAMEO [1].

Traditionally, Electric Machine design has primarily focused on factors like efficiency, packaging, and cost, often neglecting the critical aspects of Noise, Vibration, and Harshness (NVH) in the early decision-making stages. This disconnect between E-Machine design teams and NVH teams has consistently posed a challenge. This paper introduces an innovative workflow that unifies these previously separate domains, facilitating comprehensive optimization by seamlessly integrating NVH considerations with other E-Machine objectives, such as electromagnetic compatibility (EMC). This paper highlights AVL's approach in achieving this transformation and demonstrates how this integrated approach sets a new standard for E-Machine design. The presented approach relies on AI-driven algorithms and computational tools.

Within gear pair development, the simulation of loaded transmission error, line of action and summarized mesh point are crucial information in design optimization as well as reliability, NVH and efficiency prediction. These properties and variables are difficult to evaluate and are usually only assessed through proxy-variables such as unloaded transmission error or contact pattern assessment. Alternatively, large design loops can be generated when prototypes are produced to directly assess the results of reliability, NVH and efficiency and simulation models updated to the results, but not directly calibrated. This work will showcase an advanced test facility with the unique capabilities to evaluate all gear contact types (including hypoid, beveloid, cylindrical and spiral) under loaded conditions while assessing position and force data that can be used to validate simulation models directly and enhance design development.

Today, the number of downsized engines with two or three cylinders is increasing due to an increase in fuel efficiency. However, downsized engines exhibit unbalanced interior sound in the range of their optimal engine speed, largely because of their dominant engine orders. In particular, the sound of two-cylinder engines yields half the perceived engine speed of an equivalent four-cylinder engine at the same engine speed. As a result when driving, the two-cylinder engine would be shifted to higher gears much later, diminishing the expected fuel savings. This contribution presents an active in-car sound generation system that makes a two-cylinder engine sound like the more familiar four-cylinder engine. This is done by active, load-dependent playback of signals extracted from the engine vibration through a shaker mounted on the firewall. A blind test with audio experts indicates a significant reduction of the engine speed when shifting to a higher gear.

Rising gas prices and increasingly stringent vehicle emissions standards have pushed automakers to increase fuel economy. Mass reduction is the most practical method to increase fuel economy of a vehicle. New materials and CAE technology allow for lightweight automotive components to be designed and manufactured, which outperform traditional component designs. Topology optimization and other design optimization techniques are widely used by designers to create lightweight structural automotive parts. Other design optimization techniques include free-size, gauge, and size optimization. These optimization techniques are typically used in sequence or independently during the design process. Performing various types of design optimization simultaneously is only practical in certain cases, where different parts of the structure have different manufacturing constraints.

The aim of this paper is to present the adjoint equations for shape optimization derived from steady incompressible Navier-Stokes (N-S) equations and an objective functional. These adjoint Navier-Stokes equations have a similar form as the N-S equations, while the source terms and the boundary conditions depend on the chosen objective. Additionally, the gradient of the targeted objective with respect to the design variables is calculated. Based on this, a modification of the geometry is computed to arrive at an improved objective value. In order to find out, whether a more sophisticated approach is needed, the adjoint equations are derived by using two different approaches. The first approach is based on the frozen turbulence assumption and the second approach, which is advanced in this paper, is derived from the near wall k − ζ − f turbulence model.

This paper describes the simulation tool chain serving to design and optimize the transmission of an electric axle drive from concept to final design with respect to NVH. A two-stage transmission of an eAxle is designed from scratch by the initial layout of gears and shafts, including the optimization of gear micro geometry. After the shaft system and bearings are defined, the concept design of the transmission housing is evaluated with the help of a basic topology optimization regarding stiffness and certain eigenfrequencies. In the next step a fully flexible multi-body dynamic (MBD) and acoustic analysis of the transmission is performed using internally calculated excitations due to gear contact and bearing interaction with shaft and gear dynamics for the entire speed and load range. Critical operating conditions in terms of shaft dynamics, structure borne noise and noise radiation are evaluated and selected as target for optimization in the following steps.

The flame propagation characteristic is one of the greatest factor that determines the performance of spark ignition (SI) engines. The in-cylinder flow dynamics is very significant in terms of flame propagation because of its direct influence on the flame shape, turbulent flame speed, and the ignition quality. A number of different techniques are available to optimize the in-cylinder flow and maximize the utilization of turbulence for faster combustion, and tumble enhancement by intake port geometry is one of them. It requires excessive computational expenses to evaluate multiple designs under wide range of operating conditions by 3D-CFD, therefore, a low-dimensional model would be more competitive in such design optimization process. This work suggests a new modification approach for typical 0D turbulence model to take account for the tumble generation during the intake process as well as the turbulence characteristics associated with it.

Precise three-dimensional dummy head trajectories during crash tests are very important for vehicle safety development. To determine precise trajectories with a standard deviation of approximately 5 millimeters, three-dimensional video analysis is an approved method. Therefore the tracked body is to be seen on at least two cameras during the whole crash term, which is often not given (e.g. head dips into the airbag). This non-continuity problem of video analysis is surmounted by numerical integration of differential un-interrupted electrical rotation and acceleration sensor signals mounted into the tracked body. Problems of this approach are unknown sensor calibration errors and unknown initial conditions, which result in trajectory deviations above 10 centimeters.

In the virtual development process validated simulation models are requested to accurately predict power train vibration and comfort phenomena. Conclusions from refined parameter studies enable to avoid costly tests on rigs and on the road. Thereby, an appropriate modeling approach for specific phenomena has to be chosen to ensure high quality results. But then, parameters for characterizing the dynamic properties of components are often insufficient and have to be roughly estimated in this development stage. This results in a imprecise prediction of power train resonances and in a less conclusive understanding of the considered phenomena. Conclusions for improvements remain uncertain. This paper deals with the two different aspects of model refinement and parameter updating. First an existing power train model (predecessor power train) is analyzed whether the underlying modeling approach can reproduce the physical behavior of the power train dynamics adequately.

Optimizing the specifications of the parts that make up the vehicle is essential to develop a high performance and quality vehicle with price competitiveness. Optimizing parts specifications for quality and affordability means optimizing various factors such as engineering design specifications and manufacturing processes of parts. This optimization process must be carried out in the early stages of development to maximize its effectiveness. Therefore, in this paper, we studied the methodology of building a database for parts of already developed vehicles and optimizing them on a data basis. A methodology for collecting, standardizing, and analyzing data was studied to define information necessary for specification optimization. In addition, AI technology was used to derive optimization specifications based on the 3D shape of the parts. Through this study, body parts specification optimization system using AI technology was developed.

A worldwide decrease of legal limits for CO2 emissions and fuel economy led to stronger efforts for achieving the required reductions. The task is to evaluate technologies for CO2 reduction and to define a combination of such measures to ensure the targets. The challenge therefor is to find the optimal combination with respect to minimal costs. Individual vehicles as well as the whole fleet have to be considered in the cost analysis - which raises the complexity. Hereby, the focus of this work is the consideration and improvement of a new model series against the background of a fleet and the selection of measures. The ratio between the costs and the effect of the measures can be different for the each vehicle configuration. Also, the determination of targets depends whether a fleet or an individual vehicle is selected and has impact on the selection and optimization process of those measures.

Energy politics and environmental circumstances demand novel strategies for private transport. Several studies have shown that one of these possibilities can be an electric vehicle with a range extender - REX. Today these REX engines are under way as derivation from modern internal combustion engines. As the need for an optimized usage of energy will further increase in the future, alternative energy converter systems have to be investigated. For DENSO, as supplier of components, it is of strong interest how the basic layout of these concepts could look like. This is necessary in order to be prepared for the specific needs of these concepts in terms of auxiliaries, electric / electronic components as well as for the cabin climate & various control strategies. In these REX-concepts all energies have to be considered. A sophisticated usage of energy inside a REX vehicle is required which leads to the investigation of a combined heat and power usage on-board.

Over the past 30 years, simulation of the N&V (Noise and Vibration) behaviour of automotive drivelines became an integral part of the powertrain development process. With current and future HEVs (Hybrid-Electrical Vehicles), additional phenomena and effects have entered the scene and need to be taken into account during layout/design as well as optimization phase. Beside effects directly associated with the e-components (namely electric whistle and whine), torque changes caused by activation/deactivation of the e-machine give rise to vibration issues (e.g. driveline shuffle or clonk) as well. This is in particular true for transient operation conditions like boosting and recuperation. Moreover, aspects of starting the Internal Combustion Engine (ICE) using the built-in e-machine in conjunction with the dynamic behaviour of torsional decoupling devices become increasingly important. In order to cope with above-mentioned effects a multi-physics simulation approach is required.

The presented paper deals with a methodology to model cycle-to-cycle variations (CCV) in 0-D/1-D simulation tools. This is achieved by introducing perturbations of combustion model parameters. To enable that, crank angle resolved data of individual cycles (pressure traces) have to be available for a reasonable number of engine cycles. Either experimental data or 3-D CFD results can be applied. In the presented work, experimental data of a single-cylinder research engine were considered while predicted LES 3-D CFD results will be tested in the future. Different engine operating points were selected - both stable ones (low CCV) and unstable ones (high CCV). The proposed methodology consists of two major steps. First, individual cycle data have to be matched with the 0-D/1-D model, i.e., combustion model parameters are varied to achieve the best possible match of pressure traces - an automated optimization approach is applied to achieve that.

State-of-the-art motorcycle engines consist of numerous variable components and require a powerful motor management to meet the growing customer expectations and the legislative requirements (e.g. exhaust and noise emissions, fuel consumption) at the same time. These demands are often competing and raise the level of complexity in calibration. In the racing domain, the optimization requirements are usually higher and test efficiency is crucial. Whilst the number of variables to control is growing, the time to perform an engine optimization remains the same or is even shortened. Therefore, simulation is becoming an essential part of the engine calibration optimization. Considering the special circumstances in racing, involving valuable hardware, as well as extremely short development and calibration iteration loops, only transient testing is possible.

Limited fossil fuel resources, air pollution, and global warming all drive strengthening of fuel economy and vehicle emission standards globally. Much R&D continues to be dedicated to improve fuel efficiency of automobiles and to reduce exhaust gasses. These include improvement of engine/driveline performance for higher efficiency, development of alternative energy, and minimization of air resistance through aerodynamic design optimization. OEM weight reduction-focused research has extended into chassis components (steering knuckle, brakes, control arms, etc.) in sequence from body-in-white(BIW). Wheel bearings, one of the core components of a driveline and part of a vehicle’s unsprung mass, are also being required to reduce weight. Conventionally, wheel bearings have achieved “lightweighting” primarily through design optimization methods. They have been highly optimized today using steel based materials.

In recent years, the use of high-power inverters has become increasingly prevalent in vehicles applications. With the increasing number of electric vehicle models comes the need for efficient and reliable testing methods to ensure the proper functioning of these inverters. One such method is the use of Hardware-in-the-Loop (HiL) environments, where the inverter is connected to a simulated environment to test its performance under various operating conditions. HiL testing allows for faster and more cost-effective testing than traditional methods and provides a safe environment to evaluate the inverter's response to different scenarios. Further, in such an environment, it is possible to specifically stimulate those system states in which conflicts between the lines arise regarding the ideal system parametrization. By combining HiL testing with design-of-experiments and modelling methods, the propulsion system can hence be optimized in a holistic manner.