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Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.

The European emission legislation for two-wheeler vehicles driven by engines of ≤ 50 cm₃ is continuously developing. One of the most important issues in the near future will be the finalization of the European Commission's proposals for future steps in the emissions regulations as well as the verification of the impacts of current standards on the market. To have a basis for the discussion about these topics, the Association for Emissions Control by Catalyst (AECC) with the Institute for Internal Combustion Engines and Thermodynamics of Graz University of Technology (IVT) carried out an extensive test program to show the actual emission situation of state-of-the-art mopeds including mass and number of particulate matter as well as unregulated gaseous components. One of the main goals of these tests was to measure exhaust emissions without any modifications to the engines of standard production vehicles available on the European market.

In hybrid electric vehicles (HEV), the operation strategy strongly influences the available system power, as well as local exhaust emissions. Predictive operation strategies rely on knowledge of future traction-force demands. This predicted information can be used to balance the battery’s state of charge or the engine’s thermal system in their legal operation limits and can reduce peak loads. Assuming the air and rolling drag-coefficient to be constant, the desired vehicle velocity, vehicle-mass and longitudinal driving resistances determine the vehicle’s traction-force demand. In this paper, a novel methodology, combining a history-based prediction algorithm for estimating future traction-force demands with the parameter identification of road grade angle and vehicle mass, is proposed. It is solely based on a route-history database and internal vehicle data, available on its on-board communication and measuring systems.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.

The optimization of aerodynamic drag represents an important research area for the fuel consumption reduction of heavy duty commercial vehicles. Today's design of tractor-trailers is significantly influenced by legal conditions regarding the vehicle dimensions and the provision of a maximum transportation volume. These boundary conditions lead to brick-shaped trailer outer geometries, especially at the rear ends. That is the reason why the investigations of aerodynamic optimization of commercial vehicle trailers are predominantly restricted to detail measures up to now. The present publication treats the aerodynamic characteristics of general modifications on the outer contour of long-distance haulage trailers in regard of reducing the drag resistance and, thus, potentially also the fuel consumption in highway traffic. A new approach for the realization of a variable outer contour of trailers provides the possibility to adjust the rear end to an aerodynamically optimized shape.

These days a new generation of hybrid electric vehicles (HEV) are penetrating the global vehicle market - the plug-in hybrid electric vehicles (PHEVs). Compared to conventional HEVs, PHEVs have additional significant potential. They are able to improve fuel efficiency and reduce local emissions due to higher battery capacities, and they can be recharged from external outlets. Energy management has a major impact on the PHEVs performance. In this publication, an innovative operation strategy for PHEVs is presented. This is due to the fact that both increasing fuel efficiency and enhancing the vehicle's longitudinal performance requires a fine balance between the consumption of fossil and electric energy. The new operation strategy combines advanced predictive and adaptive algorithms. In contrast to the charge-sustaining strategy of HEVs, the charge-depleting mode for PHEVs is more appropriate.

The absence of combustion engine noise pushes increasingly attention to the sound generation from other, even much weaker, sources in the acoustic design of electric vehicles. The present work focusses on the numerical computation of flow induced noise, typically emerging in components of flow guiding devices in electro-mobile applications. The method of Large-Eddy Simulation (LES) represents a powerful technique for capturing most part of the turbulent fluctuating motion, which qualifies this approach as a highly reliable candidate for providing a sufficiently accurate level of description of the flow induced generation of sound. Considering the generic test configuration of turbulent pipe flow, the present study investigates in particular the scope and the limits of incompressible Large-Eddy Simulation in predicting the evolution of turbulent sound sources to be supplied as source terms into the acoustic analogy of Lighthill.

Given approximately one million small and light aircraft in operation worldwide, icing detection and icing quantification of in-flight icing are still an open research topic. Despite technical means are available to de-ice on ground, there is a lack of a suitable control system based on sensor data to de-ice while the aircraft is airborne. Most often, it is still task of the pilot to visually inspect the icing status of the airfoil and/or other critical parts of the aircraft such as engine air intakes, which distracts the flight crew from flying the aircraft especially in IMC conditions. Based on preliminary simulation and tests in 2014 in a collaborative research project lasting from 2015 until 2018, the technology of energy self-sustaining, wireless, self-adhesive smart sensors for industrial sensing in an aerodynamically critical environment (i.e. wind turbines) was further investigated to fulfil general aviation requirements.

This paper provides a method that corrects errors induced by the empty-tunnel pressure distribution in the aerodynamic forces and moments measured on an automobile in a wind tunnel. The errors are a result of wake distortion caused by the gradient in pressure over the wake. The method is applicable to open-jet and closed-wall wind tunnels. However, the primary focus is on the open tunnel because its short test-section length commonly results in this wake interference. The work is a continuation of a previous paper [4] that treated drag only at zero yaw angle. The current paper extends the correction to the remaining forces, moments and model surface pressures at all yaw angles. It is shown that the use of a second measurement in the wind tunnel, made with a perturbed pressure distribution, provides sufficient information for an accurate correction. The perturbation in pressure distribution can be achieved by extending flaps into the collector flow.

Today's automotive software integration is a static process. Hardware and software form a fixed package and thus hinder the integration of new electric and electronic features once the specification has been completed. Usually software components assigned to an ECU cannot be easily transferred to other devices after they have been deployed. The main reasons are high system configuration and integration complexity, although shifting functions from one to another ECU is a feature which is generally supported by AUTOSAR. The concept of a Virtual Functional Bus allows a strict separation between applications and infrastructure and avoids source code modifications. But still further tooling is needed to reconfigure the AUTOSAR Basic Software (BSW). Other challenges for AUTOSAR are mixed integrity, versioning and multi-core support. The upcoming BMW E/E-domain oriented architecture will require all these features to be scalable across all vehicle model ranges.

In the acoustic design of flow guiding components, novel simulation concepts for predicting relevant sound sources in the early design state become increasingly important. This requires accurate numerical methods to describe the involved phenomena. The present study computationally investigates the flow-induced aeroacoustic sound sources, generated in turbulent pipe flow. The analysis follows a hybrid approach, where the acoustic sound field is predicted separately from the underlying turbulent flow field, supplied with acoustic source terms from an incompressible flow simulation of the considered configuration in the limit of low Mach number. Source terms for use as input into different acoustic wave equations, the Lighthill wave equation, the vortex sound theory, and the Perturbed Convective Wave Equation (PCWE) are computed performing incompressible Direct Numerical Simulations (DNS) and Large-Eddy Simulations (LES) of fully developed pipe flow.

The Institute for Internal Combustion Engines and Thermodynamics, Graz University of Technology, has presented several applications of its 2-stroke LPDI (low pressure direct injection) technology in the previous years ([1], [2], [3]). In order to improve the competitiveness of the 2-stroke LPDI technology, an air cooled 50cm3 scooter application has been developed. All previous applications have been liquid cooled. This air cooled application demonstrates the EURO 4 (2017) ability of the technology and shows that the 2S-LPDI technology can also be applied to low cost air-cooled engines. Hence, the complete scooter and moped fleet can be equipped with this technology in order to fulfil both the emission standards and the COP (conformity of production) requirements of Euro 4 emission stage. The paper presents the Euro 4 Scooter results and describes the efficient conversion process of the existing carburetor engine to the LPDI version.

In the automotive industry well-established different simulation tools targeting different needs are used to mirror the physical behavior of domain specific components. To estimate the overall system behavior coupling of these components is necessary. As systems become more complex, simulation time increases rapidly by using traditional coupling approaches. Reducing simulation time by still maintaining accuracy is a challenging task. Thus, a coupling methodology for co-simulation using adaptive macro step size control is proposed. Convergence considerations of the used algorithms and scheduling of domain specific components are also addressed. Finally, the proposed adaptive coupling methodology is examined by means of a cross-domain co-simulation example describing a hybrid electric vehicle. Considerable advantages in terms of simulation time reduction are presented and the trade-off between simulation time and accuracy is depicted.

Drivetrain electrification is increasing in the kart racing sector since noise emissions are an important factor in urban areas. To improve range, it has become necessary to optimize the rolling resistance of kart racing tires. This paper introduces a parameter study for small bias-ply tires which are used in kart racing and investigates the effect of these parameters on rolling resistance. In recent literature, rolling resistance is mostly examined in radial passenger car tires. Most testing devices are limited to rim sizes from ten inches upwards. In this study, a test rig was developed with focus on low cost and small rim sizes. This self-developed test rig was validated through a comparison with an approved test rig according to ISO 18164 standard. A parameter study was conducted to investigate the effect of changes in the construction of the tire. These changes affect the warp count of the carcass fabric and the crown angle of the different plies.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

The release of the “Regulation No. 168/2013” for the approval and market surveillance of two- or three-wheel motorcycles and quadricycles of the European Union started a new challenge for the motorcycle industry. One goal of the European Union is to achieve emission parity between passenger cars (EURO 6) and motorcycles (EURO 5) in 2020. The hybridization of motorcycle powertrains is one way to achieve these strict legislation limits. In the automotive sector, hybridization is well investigated and has already shown improvements of fuel consumption, efficiency and emission behavior. Equally, motorcycle applications have a high potential to improve efficiency and to meet customer needs as fun to drive as well. This paper describes a methodical approach to analyze conventional motorcycles regarding the energy and power demand for different driving cycles and driving conditions. Therefore, a dynamic or forward vehicle simulation within MATLAB Simulink is used.

A reduction in CO2 emissions and consequently fuel consumption is essential in the context of future greenhouse gas limits. With respect to the thermodynamic loss analysis of an internal combustion engine, a gap between the net indicated thermal efficiency and the brake thermal efficiency is recognizable. This share is caused by friction losses, which are the focus of this research project. The parasitic loss reduction potential by replacing the mechanical water pump with an electric coolant pump is discussed in the course of this work. This is not a novel approach in light duty vehicles, whereas in commercial vehicles a rigid drive of all auxiliaries is standard. Taking into account an implementation of a 48-V power system in the short or medium term, an electrification of auxiliary components becomes feasible. The application of electric coolant pumps on an Euro VI certified 6-cylinder in-line heavy-duty diesel engine regarding fuel economy was thus performed.

As the number of different engine and vehicle concepts for powered-two wheelers is very high and will even rise with hybridization, the simulation of emissions and fuel consumption is indispensable for further development towards more environmentally friendly mobility. In this work, an adaptive artificial neural network based predictive model for emission and fuel consumption simulation of motorcycles operated in real world conditions is presented. The model is developed in Matlab and Simulink and is integrated into a longitudinal vehicle dynamic simulation whereby it is possible to simulate various and not yet measured test cycles. Subsequently, it is possible to predict real drive emissions RDE and on-road fuel consumption by a minimum of previous measurement effort.

The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.

The BMW Group has introduced electric cars to the market with the MINI E already in 2009. The next step will be the launch of the BMW ActiveE in 2011, followed by the revolutionary Mega City Vehicle in 2013. The presentation will explain the BMW Group strategy for implementing sustainable mobility. A focus will be emobility, the use of carbon fiber and the holistic sustainability approach of BMW Group?s project i. Reference will be made to the research results of the MINI E projects in the US and in Europe. Presenter Andreas Klugescheid, BMW AG