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In the last years motorsport is facing a technical revolution concerning the engine technology in every category, from touring car championships up to the F1. The strategy of the car manufacturers to bring motorsport engine technology closer to mass production one (e.g. turbo-charging, downsizing and direct injection) allows both to reduce development costs and to create a better image and technology transfer by linking motorsport activities to the daily business. Under these requirements the so-called Global Race Engine (GRE) concept has been introduced, giving the possibility to use one unique engine platform concept as basis for different engine specifications and racing categories. In order to optimize the performance of this kind of engines, especially due to the highly complex mixture formation mechanisms related to the direct injection, it is nowadays mandatory to resort to reliable 3D-CFD simulations.

The electrification of vehicle fleets for urban delivery transport is becoming increasingly important due to ever stricter legal requirements and the high public pressure on companies. In this paper, a converted 3.5 t light-duty vehicle with a maximum gross weight of 7.5 t is presented. The vehicle has a serial hybrid electric powertrain with a maximum electric traction power of 150 kW and a 60 kW fuel cell range extender, and uses a 46 kWh battery with 400 V mean voltage level, resulting in a full electric range of 120 km. The electric drive is realized with an induction motor and a lithium-manganese-iron-phosphate (LMFP)-battery as well as a 2-speed gearbox. The fuel cell system has a fuel tank with 100 l volume and 700 bar pressure level, resulting in a total mass of around 4.2 kg of hydrogen. This enables an overall vehicle range of 400 km.

Prospective combustion engine applications require the highest possible energy conversion efficiencies for environmental and economic sustainability. For conventional Spark-Ignition (SI) engines, the quasi-hemispherical flame propagation combustion method can only be significantly optimized in combination with high excess air dilution or increased combustion speed. However, with increasing excess air dilution, this is difficult due to decreasing flame speeds and flammability limits. Pre-Chamber (PC) initiated jet ignition combustion systems significantly shift the flammability and flame stability limits towards higher dilution areas due to high levels of introduced turbulence and a significantly increased flame area in early combustion stages, leading to considerably increased combustion speeds and high efficiencies. By now, vehicle implementations of PC-initiated combustion systems remain niche applications, especially in combination with lean mixtures.

Each component of a drive train generates waste heat due to its limited efficiency. This waste heat is usually released to an air flow guided through one or more heat exchangers. So, the realized cooling air volume flow is one important characteristic value during the vehicle development process. This paper presents some of the available techniques for the measurement of cooling air volume flow in the vehicle during the different stages of an aerodynamic development process in model scale and full scale. Additionally, it provides suggestions when comparing these experimental values to CFD results.

As a promising solution to improve fuel efficiency of a long-haul heavy duty truck with diesel engine, organic Rankine cycle (ORC) based waste heat recovery system (WHR) by utilizing the exhaust gas from internal combustion engine has continuously drawn attention from automobile industry in recent years. The most attractive concept of ORC-based WHR system is the conversion of the thermal energy of exhaust gas recirculation (EGR) and exhaust gas from Tailpipe (EGT) to kinetic energy which is provided to the engine crankshaft. Due to a shift of the operating point of the engine by applying WHR system, the efficiency of the overall system increases and the fuel consumption reduces respectively. However, the integration of WHR system in truck is challenging by using engine cooling system as heat sink for Rankine cycle. The coolant mass flow rate influences strongly on the exhaust gas bypass which ensures a defined subcooling after condenser to avoid cavitation of pump.

Fulfilling exhaust emissions regulations and meet customer performance needs mainly drive the current engine development. Turbocharging system plays a key role for that. Currently turbocharging should provide highest engine power density at high engine speed by also allowing a very responsive performance at low end. This represents a trade-off in turbocharger development. A large scaled turbine allows having moderate exhaust gas back pressure for peak power region, but leading to loss of torque in low engine speed. In the last years of engine development scavenging helped to get away a bit from this trade-off as it increases the turbine mass flow and also reduces cylinder internal residual gas at low engine speed. The mostly in-use lean strategy runs air fuel ratios of closed to stoichiometric mixture in cylinder and global (pre catalyst) of λ = 1.05 to l = 1.3. This will be out of the narrow air fuel ratio band of λ = 1 to ensure NOx conversion in the 3-way-catalyst.

Until a few years ago the discussion of reduction of CO₂ emissions was completely out of place in motorsports. Nowadays, also in this field, car manufacturers want to investigate different approaches towards a more responsible and sustainable concept. For this target an interesting and feasible solution is the use of methane as an alternative fuel. At the 2009 edition of the 24-hour endurance race of the Nürburgring the Volkswagen Motorsport GmbH, in addition to vehicles powered by gasoline engines, introduced two vehicles powered by turbocharged CNG engines. The aim was to prove that also an "environment-friendly" concept is able to provide the required efficiency, dynamic and reliability for a successful participation in motorsports. After the success in the 2009 edition the engagement has been continued in 2010; this time exclusively with CNG vehicles.

In this article the unsteady aerodynamic properties of a 25% scale DrivAer notchback model as well as the influence of the wind tunnel environment on the resulting unsteady aerodynamic forces and moments under crosswind excitation are investigated using experimental and corresponding numerical methods. Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS) swing® (side wind generator) is used to reproduce the essential properties of natural stochastic crosswind in the open jet test section of the Institute for Internal Combustion Engines and Automotive Engineering (IVK) model scale wind tunnel (MWK). The results show that the test environment of an open jet wind tunnel alters the amplitudes of side force and yaw moment under crosswind excitation when compared to an ideal environment neglecting wind tunnel interference effects.

The field of vision of the driver during wet road conditions is essential for safety at all times. Additionally, the safe use of the increasing number of sensors integrated in modern cars for autonomous driving and intelligent driver assistant systems has to be ensured even under challenging weather conditions. To fulfil these requirements during the development process of new cars, experimental and numerical investigations of vehicle soiling are performed. This paper presents the surface contamination of self- and foreign-soiling tested in the wind tunnel. For these type of tests, the fluorescence method is state-of-the-art and widely used for visualizing critical areas. In the last years, the importance of parameters like the contact angle have been identified when designing the experimental setup. In addition, new visualization techniques have been introduced.

The aeroacoustic development of vehicles is still mainly carried out in wind tunnels under steady flow conditions, although the real situation is different. However, as discussed in several earlier publications, a vehicle experiences unsteady, turbulent flow on road, which results for example from natural wind, wakes of other vehicles, or obstacles at the roadside in combination with side wind. The resulting temporal variations of the wind noise inside the cabin affect the passengers’ comfort and safety through fatigue. To be able to also consider the unsteady aeroacoustics in the vehicle development process, a comprehensive method has been developed that is presented in full for the first time in this paper. The on-road situation is simulated in a realistic and reproducible manner in the full-scale wind tunnel of the University of Stuttgart by means of an active turbulence generator, developed by FKFS.

In the automotive industry, there is a continued need to improve the development process and handle the increasing complexity of the overall vehicle system. One major step in this process is a comprehensive and complementary approach to both simulation and testing. Knowledge of the overall dynamic vehicle behavior is becoming increasingly important for the development of new control concepts such as integrated vehicle dynamics control aiming to improve handling quality and ride comfort. However, with current well-established test systems, only separated and isolated aspects of vehicle dynamics can be evaluated. To address these challenges and further merge the link between simulation and testing, the Institute of Internal Combustion Engines and Automotive Engineering (IVK), University of Stuttgart is introducing a new Handling Roadway (HRW) Test System in cooperation with The Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (FKFS) and MTS Systems Corporation.

Due to the demands for today's passenger cars regarding fuel consumption and emissions, exhaust turbo charging has become a fundamental step in achieving these goals. Especially in upper and middle class vehicles it is also necessary to consider the noise comfort. Today, floating bushings are mainly used as radial bearings in turbochargers. In the conventional operating range of the turbocharger dynamic instability occurs in the lubrication films of the bearings. This instability is transferred by structure-borne noise into audible airborne sound and known as constant tone phenomenon. This phenomenon is not the major contributor of the engine noise but its tonal character is very unpleasant. In order to gain a more detailed understanding about the origin of this phenomenon, displacement sensors have been applied to the compressor- and the turbine-side of the rotor, to be able to determine the displacement path.

In this paper, a serial/parallel hybrid electric vehicle with a 17 kWh battery and 400 V voltage level is simulated. The vehicle is a C-segment vehicle, which has optimized driving resistances. It also has an external recharge possibility, which enables fully electric driving. The vehicle uses an Otto-engine concept as well as two electric motors. One motor is a permanent magnet synchronous motor and can be used as traction motor or generator, the other one is an induction motor used as main traction motor for the vehicle. The vehicle uses a 2-speed gearbox, where the electric motors are mounted in P2-configuration. To reach optimal results for the fuel consumption, an operating strategy based on the Equivalent Consumption Minimization Strategy (ECMS) is introduced and implemented in the vehicle simulation.

This paper contributes to the Committee on Commonized Aerodynamics Automotive Testing Standards (CAATS) initiative, established by the late Gary Elfstrom. It is collaboratively compiled by automotive wind tunnel users and operators within the Subsonic Aerodynamic Testing Association (SATA). Its specific focus lies in automotive wind tunnel test techniques, encompassing both those relevant to passenger car and race car development. It is part of the comprehensive CAATS series, which addresses not only test techniques but also wind tunnel calibration, uncertainty analysis, and wind tunnel correction methods. The core objective of this paper is to furnish comprehensive guidelines for wind tunnel testing and associated techniques. It begins by elucidating the initial wind tunnel setup and vehicle arrangement within it.

High-efficient simulations are mandatory to manage the ever-increasing complexity of automotive powertrain system and reduce development time and costs. Integrating AI methods into the development process provides an ideal solution thanks to massive increase in computational power. Based on an 1D physical engine model of a turbo-charged direct injection gasoline engine with variable valve timing (VVT), a high-performance hybrid simulation model has been developed for increasing computing performance. The newly developed model is made of a physics-based low-pressure part including intake and exhaust peripheries and a neural-network-based high-pressure part for combustion chamber calculations. For the training and validation of the combustion chamber neural networks, a data set with 10.5 million operating points was generated in a short time thanks to the parallelizable combustion chamber simulations in stand-alone mode.

This paper presents an autonomous cruise control for battery electric vehicles. The presented approach is based on the usage of predictive route data which is extracted out of a digital map and a wide range radar system in order to capture vehicles in front. By using the predictive route data and the information of the radar system, the autonomous cruise control can control the vehicle's speed over a wide range of driving situations without any driver interaction. The main aim of the presented autonomous cruise control is to optimize the battery electric vehicle's energy consumption. The main idea is to use predictive route data in order to calculate a consumption optimal vehicle speed trajectory by means of online optimization. The benefits of the autonomous cruise control are shown by means of real test drives and measured data evaluation.

In this work, a novel approach is introduced comprising a combination of unsupervised machine learning (ML) scheme and charging energy management of electric vehicles (EV). The main goal of this implementation is to reduce the load peak of charging EV’s, which are regular users of electric vehicle supply equipment (EVSE) of a certain building and, at the same time, to meet their electric and behavioral demands. The unsupervised ML considers certain features within the charging profiles in addition to the behavioral characteristics of the EV based on its intended use. Moreover, these features are extracted from large sets of history measurement data of the EVSE, which are stored in the data bank. The ML categorizes the EVs within certain clusters having defined specifications.

Predictive physical modeling is an established method used in the development process for automotive components and systems. While accurate predictions can be issued after tuning model parameters, long computation times are expected depending on the complexity of the model. As requirements for components and systems continuously increase, new optimization approaches are constantly being applied to solve multidimensional objectives and resulting conflicts optimally. Some of those approaches are deemed not feasible, as the computational times for required single predictions using conventional simulation models are too high. To address this issue it is proposed to use data-driven model such as neural networks. Previous efforts have failed due to sparse data sets and resulting poor predictive ability. This paper introduces an AI Toolchain used for data-driven modeling of combustion engine components. Two methods for generating scalable and fully variable datasets will be shown.

In order to decarbonize and lower the overall emissions of the transport sector, immediate and cost-effective powertrain solutions are needed. Natural gas offers the advantage of a direct reduction of carbon dioxide (CO2) emissions due to its better Carbon to Hydrogen ratio (C/H) compared to common fossil fuels, e.g. gasoline or diesel. Moreover, an optimized engine design suiting the advantages of natural gas in knock resistance and lean mixtures keeping in mind the challenges of power density, efficiency and cold start manoeuvres. In the public funded project MethMag (Methane lean combustion engine) a gasoline fired three-cylinder-engine is redesigned based on this change of requirements and benchmarked against the previous gasoline engine.

In this paper the development approach and the results of numerical and experimental investigations on homogeneous charge compression ignition in a free piston engine are presented. The Free Piston Linear Generator (FPLG) is a new type of internal combustion engine designed for the application in a hybrid electric vehicle. The highly integrated system consists of a two-stroke combustion unit, a linear generator, and a mass-variable gas spring. These three subsystems are arranged longitudinally in a double piston configuration. The system oscillates linearly between the combustion chamber and the gas spring, while electrical energy is extracted by the centrally arranged linear generator. The mass-variable gas spring is used as intermediate energy storage between the downstroke and upstroke. Due to this arrangement piston stroke and compression ratio are no longer determined by a mechanical system.