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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 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.

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.

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.

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.

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.

Hybrid-electric vehicles provide additional functionality compared to conventional vehicles. So-called ‘hybrid’ software functions are required to coordinate the conventional powertrain control and these additional control functions. A key factor to reduce the fuel consumption lies in optimal control of the entire interconnected powertrain. This paper aims to provide a framework for efficient interface definition, connection and coordination of control units for hybrid electric vehicles. Such a framework supports an efficient development of control unit architectures and the distribution of software functions. The generic approach necessitates modular software functions. It defines the distribution of these functions in control units optimized with respect to reuse, interfaces and compatibility with different powertrain topologies and electrification variants, especially also considering compatibility with a conventional powertrain and its electric hybridization.

One of the most important safety issues for automotive engineering is to avoid any fire due to the ignition of flammable liquids, which may result from leaks. Fire risk is a combination of hot temperature, fast vaporisation and accumulation of vapor in a cavity. In IC engines, potentially flammable liquids are fuel and oil. To guarantee safety, flammable liquids must not come into contact with hot parts of the engine. Consequently, shields are designed to guide the flow path of possible leakages and to take any flammable liquid out of the hot areas. Simulation is a great help to optimize the shape of the shield by investigating a large number of possible leakages rapidly. Recent breakthroughs in numerical methods make it possible to apply simulations to industrial design concepts. The employed approach is based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) method.

Five very different exhaust ports of diesel and gasoline engines are investigated under steady and unsteady flow to determine whether their flow coefficients are sensitive to unsteady flow. Valve lift is fixed for a specific test but varied from test to test to determine whether the relationship between steady and unsteady flow is lift dependent. The pulse frequency is chosen to correspond to the blow-down phase of an engine running at approximately 6000 rpm, but the pressure drop across the port is much smaller than that present in a running engine. Air at room temperature is used as the working fluid. It is shown that unsteady flow through the five exhaust ports causes, at most, a 6% increase or a 7% decrease in flow coefficient.

Dynamometer (dyno) durability testing plays a significant role in reliability and durability assessment of commercial engines. Frequently, durability test procedures are based on warranty history and corresponding component failure modes. Evolution of engine designs, operating conditions, electronic control features, and diagnostic limits have created challenges to historical-based testing approaches. A physics-based methodology, known as Load Matrix, is described to counteract these challenges. The technique, developed by AVL, is based on damage factor models for subsystem and component failure modes (e.g. fatigue, wear, degradation, deposits) and knowledge of customer duty cycles. By correlating dyno test to field conditions in quantifiable terms, such as customer equivalent miles, more effective and efficient durability test suites and test procedures can be utilized. To this end, application of Load Matrix to a heavy-duty diesel engine is presented.

This works presents a real-time capable simulation model for dual fuel operated engines. The computational performance is reached by an optimized filling and emptying modeling approach applying tailored models for in-cylinder combustion and species transport in the gas path. The highly complex phenomena taking place during Diesel and gasoline type combustion are covered by explicit approaches supported by testbed data. The impact of the thermodynamic characteristics induced by the different fuels is described by an appropriate set of transport equations in combination with specifically prepared property databases. A thermodynamic highly accurate 6-species approach is presented. Additionally, a 3-species and a 1-species transport approach relying on the assumption of a lumped fuel are investigated regarding accuracy and computational performance. The comparison of measured and simulated pressure and temperature traces shows very good agreement.

Today's powertrains are becoming more and more complex due to the increasing number of gear box types requiring gearshift patterns like conventional (equipped with GSI) and automatic-manual transmissions (AT, AMT), double clutch and continuous variable transmissions (DCT, CVT). This increasing variety of gear boxes requires a higher effort for the overall optimization of the powertrain. At the same time, it is necessary to assess the impact of different powertrains and control strategies on CO₂ emissions very early in the development process. The optimization of Gear Shift Patterns (G.S.P.) has to fulfill multiple constraints in terms of objective customers' requirements, like driveability, NVH, performance, emissions and fuel consumption. For these reasons, RENAULT and AVL entered an engineering collaboration in order to develop a dedicated simulation tool: CRUISE GSP.

The need for significant reduction of fuel consumption and CO₂ emissions has become the major driver for development of new vehicle powertrains today. For the medium term, the majority of new vehicles will retain an internal combustion engine (ICE) in some form. The ICE may be the sole prime mover, part of a hybrid powertrain or even a range extender; in every case potential still exists for improvement in mechanical efficiency of the engine itself, through reduction of friction and of parasitic losses for auxiliary components. A comprehensive approach to mechanical efficiency starts with an analysis of the main contributions to engine friction, based on a measurement database of a wide range of production engines. Thus the areas with the highest potential for improvement are identified. For each area, different measures for friction reduction may be applicable with differing benefits.

Due to increasing complexity of engine electronic systems, there is a demand to handle the often more than 10,000 calibration data automatically. Establishing optimized start of injection and EGR tables of a TC DI Diesel engine by conventional methods takes about two weeks of intensive calibration work. By automatic map calibration, this task can be handled in less than 20 hours automatically, with no staff required during optimization. The benefits of automatic calibration therefore are reduced costs and faster response to any changes in parameters, even with complex multidimensional engine calibration problems. The paper describes the optimization method as well as the experimental work on the test stand that produces the results.