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The application of state-of-art thermoelectric generator (TEG) in automotive engine has potential to reduce more than 2% fuel consumption and hence the CO2 emissions. This figure is expected to be increased to 5%~10% in the near future when new thermoelectric material with higher properties is fabricated. However, in order to maximize the TEG output power, there are a few issues need to be considered in the design stage such as the number of modules, the connection of modules, the geometry of the thermoelectric module, the DC-DC converter circuit, the geometry of the heat exchanger especially the hot side heat exchanger etc. These issues can only be investigated via a proper TEG model. The authors introduced four ways of TEG modelling which in the increasing complexity order are MATLB function based model, MATLAB Simscape based Simulink model, GT-power TEG model and CFD STAR-CCM+ model. Both Simscape model and GT-Power model have intrinsic dynamic model performance.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values. This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).

The drag coefficient at zero yaw angle is the single parameter usually used to define the aerodynamic drag characteristics of a passenger car. However, this is usually the minimum drag condition and will, for example, lead to an underestimate of the effect of aerodynamic drag on fuel consumption because the important influence of the natural wind has been excluded. An alternative measure of aerodynamic drag should take into account the effect of nonzero yaw angles and a variant of wind-averaged drag is suggested as the best option. A wind-averaged drag coefficient (CDW) is usually derived for a particular vehicle speed using a representative wind speed distribution. In the particular case where the road speed distribution is specified, as for a drive cycle to determine fuel economy, a relevant drag coefficient can be derived by using a weighted road speed.

Electrification of aircraft is on track to be a future key design principal due to the increasing pressure on the aviation industry to significantly reduce harmful emissions by 2050 and the increased use of electrical equipment. This has led to an increased focus on the research and development of alternative power sources for aircraft, including fuel cells. These alternative power sources could either be used to provide propulsive power or as an Auxiliary Power Unit (APU). Previous studies have considered isolated design cases where a fuel cell system was tailored for their specific application. To accommodate for the large variation between aircraft, this study covers the design of an empirical model, which will be used to size a fuel cell system for any given aircraft based on basic design parameters. The model was constructed utilising aircraft categorisation, fuel cell sizing and balance of plant sub-models.

Beside hard facts as performance, emissions and fuel consumption especially the brand specific attributes such as styling and sound are very emotional, unique selling prepositions. To develop these emotional characters, within the given boundary conditions of the future pass-by regulation, it is necessary to define them at the very beginning of the project and to follow a consequent development process. The following paper shows examples of motorcycle NVH development work on noise cleaning and sound engineering using a hybrid development process combining front loading, simulation and testing. One of the discussed solutions is the investigation of a piston pin offset in combination with a crankshaft offset for the reduction of friction. The optimization of piston slap noise as a result of the piston secondary motion was performed by simulation. As another example a simulation based development was performed for the exhaust system layout.

The electrification of the powertrain is a prerequisite to meet future fuel consumption limits, while the internal combustion engine (ICE) will remain a key element of most production volume relevant powertrain concepts. High volume applications will be covered by electrified powertrains. The range will include parallel hybrids, 48V- or High voltage Mild- or Full hybrids, up to Serial hybrids. In the first configurations the ICE is the main propulsion, requiring the whole engine speed and load range including the transient operation. At serial hybrid applications the vehicle is generally electrically driven, the ICE provides power to drive the generator, either exclusively or supporting a battery charging concept. As the ICE is not mechanically coupled to the drive train, a reduction of the operating range and thus a partial simplification of the ICE is achievable.

In the present work, a scalable simulation methodology is presented that enables the assessment of the impact of SI-engine cycle-to-cycle combustion variations on fuel consumption and hence CO2 emissions on three different levels of modeling depth: in-cylinder, steady-state engine and transient engine and vehicle simulation. On the detailed engine combustion chamber level, a 3D-CFD approach is used to study the impact of the turbulent in-cylinder flow on the cycle-resolved flame propagation characteristics. On engine level, cycle-to-cycle combustion variations are assessed regarding their impact on indicated mean effective pressure, aiming at estimating the possible fuel consumption savings when cyclic variations are minimized. Finally, on the vehicle system level, a combined real-time engine approach with crank-angle resolved cylinder is used to assess the potential fuel consumption savings for different vehicle drivecycle conditions.

To achieve future fleet CO2 emission targets, all powertrain types, including those with internal combustion engines, need to achieve higher efficiency. Next to others the reduction of friction is one contributor to increase powertrain efficiency. The piston bore interface (PBI) accounts for up to 50 % of the total engine friction losses [1]. Optimizations in this area combined with the use of low viscosity oil, which can reduce the friction of further engine sub-systems, will therefore have a high positive impact. To assess the friction of the PBI whilst considering cross effects of other relevant parameters for mechanical function (e.g. blow-by & wear) and emissions (e.g. oil consumption) AVL has established a holistic development method based around the AVL FRISC (FRIction Single Cylinder) engine with a floating liner measurement concept.

Upcoming, increasingly stringent greenhouse gas (GHG) as well as emission limits demand for powertrain electrification throughout all vehicle applications. Increasing complexity of electrified powertrain architectures require an overall system approach combining modular component technology with integration and industrialization requirements when heading for further significant efficiency optimization. At the same time focus on reduced development time, product cost and minimized additional investment demand reuse of current production, machining, and assembly facilities as far as possible. Up to date additive manufacturing (AM) is an established prototype component, as well as tooling technology in the powertrain development process, accelerating procurement time and cost, as well as allowing to validate a significantly increased number of variants. The production applications of optimized, dedicated AM-based component design however are still limited.

The aerodynamic drag characteristics of a passenger car are typically defined by a single parameter, the drag coefficient at zero yaw angle. While this has been acceptable in the past, it may not allow a true comparison between vehicles with regard to the impact of drag on performance, especially fuel economy. An alternative measure of aerodynamic drag should take into account the effect of non-zero yaw angles and some proposals have been made in the past, including variations of wind-averaged drag coefficient. For almost all cars the drag increases with yaw, but the increase can vary significantly between vehicles. In this paper the effect of various parameters on the drag rise with yaw are considered for a range of different vehicle types. The increase of drag with yaw is shown to be an essentially induced drag, which is strongly dependent on both side force and lift. Shape factors which influence the sensitivity of drag with yaw are discussed.

DiMethyl Ether (DME) has been known to be an outstanding fuel for combustion in diesel cycle engines for nearly twenty years. DME has a vapour pressure of approximately 0.5MPa at ambient temperature (293K), thus it requires pressurized fuel systems to keep it in liquid state which are similar to those for Liquefied Petroleum Gas (mixtures of propane and butane). The high vapour pressure of DME permits the possibility to optimize the fuel injection characteristic of direct injection diesel engines in order to achieve a fast evaporation and mixing with the charged gas in the combustion chamber, even at moderate fuel injection pressures. To understand the interrelation between the fuel flow inside the nozzle spray holes tests were carried out using 2D optically accessed nozzles coupled with modelling approaches for the fuel flow, cavitation, evaporation and the gas dynamics of 2-phase (liquid and gas) flows.

For nearly twenty years, DiMethyl Ether has been known to be an outstanding fuel for combustion in diesel cycle engines. Not only does it have a high Cetane number, it burns absolutely soot free and produces lower NOx exhaust emissions than the equivalent diesel. However, the physical properties of DME such as its low viscosity, lubricity and bulk modulus have negative effects for the fuel injection system, which have both limited the achievable injection pressures to about 500 bar and DME's introduction into the market. To overcome some of these effects, a common rail fuel injection system was adapted to operate with DME and produce injection pressures of up to 1000 bar. To understand the effect of the high injection pressure, tests were carried out using 2D optically accessed nozzles. This allowed the impact of the high vapour pressure of DME on the onset of cavitation in the nozzle hole to be assessed and improve the flow characteristics.

The paper describes a zero-dimensional crank angle resolved combustion model which was developed for the analysis and prediction of combustion in compression ignition (CI) engines. The model relies on the multi zone combustion model (MZCM) approach of Hiroyasu. The main sub-models were taken from literature and extended with additional features described in this paper. A special procedure described in a previous paper is used to identify the mechanisms of the combustion process on the basis of the measured cylinder pressure trace. Based on the identified mechanisms the present work concentrates on the analysis of the causal effects that predominantly control the combustion process and the formation of NOx and Soot. The focus lies on the changes of the thermodynamic states and the composition of the reaction zones caused by different emission control strategies.

This paper presents a novel approach to augment existing engine calibrations to deliver improved engine performance during a transient, through the application of multi-objective optimization techniques to the calibration of the Variable Valve Timing (VVT) system of a 1.0 litre gasoline engine. Current mature calibration approaches for the VVT system are predominantly based on steady state techniques which fail to consider the engine dynamic behaviour in real world driving, which is heavily transient. In this study the total integrated fuel consumption and engine-out NOx emissions over a 2-minute segment of the transient Worldwide Light-duty Test Cycle are minimised in a constrained multi-objective optimisation framework to achieve an updated calibration for the VVT control. The cycle segment was identified as an area with high NOx emissions.

The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

Today, OEMs are challenged with an increasing number of powertrain variants and complexity of controls software. They are facing internal pressure to provide mature and refined calibrations earlier in the development process. Until now, it was difficult to respond to these requests as the drivability's calibration tasks are mostly done in vehicles. This paper describes a new methodology designed to answer these challenges by performing automated shift quality calibration prior to the availability of vehicles. This procedure is using a powertrain dynamometer coupled with a real-time vehicle dynamics model. By using a Power Train Test Bed (PTTB), a physical vehicle is not required. As soon as the vehicle dynamics model and its parameters have been defined, it can be simulated on the PTTB and drivability calibrations can be developed. A complete powertrain is coupled with low inertia and highly dynamic dynamometers.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

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.

Model-based calibration (MBC) is a systematic method to calibrate an engine control unit (ECU) system. Due to the working principle of MBC, it is only being used for steady-state systems (time independent models). This limits the use of MBC; because an ECU contains statistical and dynamical systems. Due to the limitations of MBC, dynamical systems require manual tuning which may be time-consuming. With the increasing popularity in hybrid and electrical vehicle systems, most of them rely on dynamical systems. Therefore, MBC is about to be superseded by manual parameterization methods. Remarkably, MBC is not limited to the steady state systems. It can be achieved by separating the time factor of a system and extracting the statistical data from a time series measurement. Typically, MBC model is conceived as the representation of a system plant (i.e.: air path, fuel path, mean value engine model). As a matter of fact, MBC model is not limited to identification of system plant.