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The 912 engine is a well known 4-cylinder horizontally opposed 4-stroke liquid-/air-cooled aircraft engine. The 912 family has a strong track record: 40 000 engines sold / 25 000 still in operation / 5 million flight hours annually. 88% of all light aircraft OEMs use Rotax engines. The 912iS is an evolution of the Rotax 912ULS carbureted engine. The “i” stands for electronic fuel injection which has been developed according to flight standards, providing a better fuel efficiency over the current 912ULS of more than 20% and in a range of 38% to 70% compared to other competitive engines in the light sport, ultra-light aircraft and the general aviation industry. BRP engineers have incorporated several technology enhancements. The fully redundant digital Engine Control Unit (ECU) offers a computer based electronic diagnostic system which makes it easier to diagnose and service the engine.

The current work investigates a method in 1D modeling of cooling systems including discretized cooling package with non-uniform boundary conditions. In a stacked cooling package the heat transfer through each heat exchanger depends on the mass flows and temperature fields. These are a result of complex three-dimensional phenomena, which take place in the under-hood and are highly non-uniform. A typical approach in 1D simulations is to assume these to be uniform, which reduces the authenticity of the simulation and calls for additional calibrations, normally done with input from test measurements. The presented work employs 3D CFD simulations of complete vehicle in STAR-CCM+ to perform a comprehensive study of mass-flow and thermal distribution over the inlet of the cooling package of a Volvo FM commercial vehicle in several steady-state operating points.

An advanced catalytic exhaust after-treatment system addresses the problem of NOX emissions from heavy-duty diesel trucks, relying on real-time catalyst modelling. The system consists of de-NOX catalysts, a device for injection of a reducing agent (diesel fuel) upstream the catalysts, and computer programmes to control the injection of the reducing agent and to model the engine and catalysts in real time. Experiments with 5 different air-assisted injectors were performed to determine the effect of injector design on the distribution of the injected diesel in the exhaust gas stream. A two-injector set-up was investigated to determine whether system efficiency could be increased without increasing the amount of catalyst or the amount of reducing agent necessary for the desired outcome. The results were verified by performing European standard transient cycle tests as well as stationary tests.

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot.

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.

An engine model for use in computer simulation of transient behavior in drivetrain and vehicle systems is presented. Two elements, important for deviation (e.g. turbo-lag) from steady state characteristics, are the inertia of the supercharging unit (turbo shaft) and the fuel injection control system. No extensive combustion calculations are carried out within the model. Instead it uses condensed results from existing combustion models and measurements. The model is semi-empirical. Some of the engine specific properties needed for simulation are (e.g. for a turbocharged diesel): engine data in steady state operation, mappings of compressor and turbine performance, inertia of the engine components condensed to the crankshaft, turbo shaft inertia, displacement, compression ratio and the essentials of the fuel injection control strategy. Input parameters to the computer program based on the model are accelerator pedal position and external torque acting on the flywheel.

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.

Squeak and rattle (S&R) are nonstationary annoying and unwanted noises in the car cabin that result in considerable warranty costs for car manufacturers. Introduction of cars with remarkably lower background noises and the recent emphasis on electrification and autonomous driving further stress the need for producing squeak- and rattle-free cars. Automotive manufacturers use several road disturbances for physical evaluation and verification of S&R. The excitation signals collected from these road profiles are also employed in subsystem shaker rigs and virtual simulations that are gradually replacing physical complete vehicle test and verification. Considering the need for a shorter lead time and the introduction of optimisation loops, it is necessary to have efficient and inclusive excitation load cases for robust S&R evaluation.

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.

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.

Existing battery parameter model structures are evaluated by estimating model parameters on real driving data applying standard system identification methods. Models are then evaluated on the test data in terms of goodness of fit and RMSE in voltage predictions. This is different from previous battery model evaluations where a common approach is to train parameters using standardized tests, e.g. hybrid pulse-power capability (HPPC), with predetermined charge and discharge sequences. Equivalent linear circuit models of different complexity were tested and evaluated in order to identify parameter dependencies at different state of charge levels and temperatures. Models are then used to create voltage output given a current, state of charge and temperature. The average accuracy of modelling the DC bus voltage provides a model goodness of fit average higher than 90% for a single RC circuit model.

Car driver’s behavior and its influence on driving characteristics play an increasing role in the development of modern vehicles, e.g. in view of efficient powertrain control and implementation of driving assistance functions. In addition, knowledge about actual driving style can provide feedback to the driver and support efficient driving or even safety-related measures. Driving patterns are caused not only by the driver, but also influenced by road characteristics, environmental boundary conditions and other traffic participants. Thus, it is necessary to take the driving circumstances into account, when driving patterns are studied. This work proposes a methodology to cluster and evaluate driving patterns under consideration of vehicle-related parameters (e.g. acceleration and jerk) in combination with additional influencing factors, e.g. road style and inclination. Firstly, segmentation of the trip in distance series is performed to generate micro cycles.